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University Research and Startup Development with Forge

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说明

Universities worldwide have used Forge in architecture, engineering, construction, and manufacturing to address research challenges and build start-up businesses around design and visualization, augmented reality and virtual reality, collaboration and interoperability, machine learning, and much more. Come and hear an international panel of researchers talking about how they've been applying Forge.

主要学习内容

  • Learn about Forge benefits for researchers and startup businesses
  • Learn how to connect and learn from international researchers about Forge implementation in different areas
  • Share ideas regarding how to build scalable solutions using Forge
  • Learn about different ways of transitioning business and research from desktop to the cloud

讲师

  • Natalia Polikarpova 的头像
    Natalia Polikarpova
    Natalia Polikarpova is a Senior Manager at Autodesk Developer Advocacy and Support team for Forge and Autodesk Developer Network (ADN). She has been with Autodesk for over 13 years now, taking care of software development companies – third parties who customize, complement and extend Autodesk desktop and web technologies. Natalia is located in Boston area, Massachusetts, USA, and is originally from Russia where she joined Autodesk as an ADN Program Manager for Russia, CIS and Eastern Europe. Prior to Autodesk, she worked in marketing and sales for a startup software development company in Moscow and state universities in Russia. She received her MBA with a concentration in management from Framingham State University, MA, USA.
  • Johannes Braumann
    Johannes Braumann and Sigrid Brell-Cokcan founded the Association for Robots in 2011 with the goal of making robots accessible to the creative industry. RiA acts as a network for creative robot users, connecting them with industry and each other, while also developing accessible software for robot programming and simulation. Both aspects have since gone far beyond the initial scope of creative users, with industry becoming increasingly interested in innovative solutions for mass customization and lot size one. Johannes is the lead developer of KUKA|prc, a solution for controlling and simulating industrial robots from within visual programming environments. It is now being used in a wide variety of industries, enabling customized, parametric production processes beyond CAD-CAM, from multi-axis 3D printing to large-scale building construction. Since 2017 Johannes holds a professorship for Creative Robotics at UfG Linz, working closely with the Ars Electronica Center and KUKA Robotics.
  • Alex Mathews
    Chief Technology Officer of FactoryFour. I work with a team of software and mechanical engineers to develop innovative solutions for digital manufacturing.
  • Alex Braun
    Alex Braun hat nach seinem Studium des Bauingenieurwesens 2013 eine Stelle als wissenschaftlicher Mitarbeiter beim Lehrstuhl für Computergestützte Modellierung und Simulation der TU München angenommen. Dort forscht er im Bereich der automatisierten Baufortschrittskontrolle. Seit 2017 ist er akademischer Rat und Leiter der Rechnerbetriebsgruppe der Fakultät Bau Geo Umwelt.
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    Transcript

    NATALIA POLIKARPOVA: Hi, everyone. Thank you so much for coming. You know you're so strong. That's the last class for today, right? It's been such a great day. Do you agree with me? Right? So yeah. And I hope you still have some energy, because the projects we are going to show you are really so exciting. My name is Natalia Polikarpova. I am with Forge Team at Autodesk.

    And just a few things to check with you-- how many of you are really new with Forge? This is great. Really welcome. That's a great place to start, to actually see what's possible. Any software developers? OK. Faculty people, students? OK. Just a few. Who are the rest?

    All right. Again, thank you very much for coming. So just to make sure you're in the right class, because, again, it's 5 o' clock. Maybe you'll miss something. We're going to talk about university research and startup opportunities with Forge. And you're going to see a few really interesting projects that our university partners developed. And they all come from different countries-- in Europe, from China, from USA.

    And what we really want you to take from this class today is to get excited by these different areas where you can go with Forge, build some really interesting experiences, and use it either for research, or for setting up your businesses. So kind of understanding what the benefits are for startups, for research areas, and see these scalable solutions that are using Forge.

    The agenda is very easy. It's just three things, what we planned. We want each of our speakers to present their projects. Then we have some questions prepared for them. And then we'll just open for questions from the audience. Are you happy with that? All right. OK. Let's go.

    So we have four speakers today. You see their pictures, you see their names. You're going to see them very quickly. So the first one I'd like to welcome is Alex Mathews, a graduate of Johns Hopkins University.

    ALEX MATHEWS: Hello. Can you guys hear me OK? All right. So my name is Alex. I was a graduate of Johns Hopkins University where I studied biomedical engineering. And I'm currently the CTO of Factory Four. At Factory Four, we have a software platform that manages data for custom fabricators. So typically, a custom fabricator makes high-mix and low-volume products. So things like orthotics and prosthetics devices, custom eyewear, aerospace components, or even dental components.

    We got started, actually, when I met my co-founder when I was an undergrad at Hopkins. We got started with a team of Johns Hopkins students. And eventually we graduated all from the university, and we're still working today as full-time employees. So should I just-- yeah.

    So I actually have a video. So this video is going to go walk through a quick introduction of the pipeline that we set up for one of our clients, which is called PQ Eyewear. PQ Eyewear is a custom eyewear manufacturer. They go all the way from a scan to a final customized product that fits perfectly and is optically accurate.

    So they hired us to build that entire software pipeline. They provided us a scanner, they had the printing facility set up. But they didn't have a scalable process to go from point A to point B. So this worked out quite well for me, because during undergrad I was doing research into cranial implants. So I was working with anatomical models and understood what processes had to go into place to generate automatic designs.

    So we use a couple tools. One was Autodesk Forge. But the other one was Autodesk Inventor. So you saw today, maybe if you were at the keynote, Autodesk Inventor is one of the newer engines that's going to be released soon in the Design Automation API. So our mechanical engineers and our software engineers work together to use the Inventor engine and to use Forge to automate and scale up a lot of the processes that our mechanical engineers do on a day-to-day basis.

    So you can see here, this is one of the designs for these eyewear frames. They are single component 3D printed frame. This is kind of like a tour. These frames are always custom-fit. And they can be printed all in one go. The challenge was, the designers who were making them before had to sit with a scan and understand how to move from one design, and then from that design inspiration, customize to exactly that person's nose and their features.

    So you can see here, this is the model pulled up in Inventor. Same thing. And this is what our mechanical engineers do in Inventor. They create the parameterized model. And this is where we define the rules of how a model changes through the various scenarios. We define the constraints, we define what those rules are, and we define what parameters we're going to be changing. And this is a collaborative process with the designers at the eyewear company to understand what do they want out of their design and how can we scale that up to fit many people in an efficient manner.

    So you can see here our mechanical engineers had to go through a very time-consuming process of creating all of the sketches required for a very complex geometry. Now, using this geometry, we're able to get a very stable model that we are able to then modify very rapidly with iLogic. Now typically, if we had stopped here and if we didn't have Forge, we would have been able to quickly generate some of these models with a CAD engineer working at one station. These are some of the example shapes that we can generate. They are very different, but they all come from the same parameterized model.

    So leveraging the iLogic in Inventor, we're able to create optimizations just with the traditional API of Inventor. So this is a well-documented API with Inventor that we are relatively familiar with programming. And working with the Autodesk engineers, we were able to understand much better how we could leverage iLogic to be able to change these parameters rapidly. So you can see here, this is actually a newer unreleased frame called the "round frame," as we've been calling it. Very similar-- a single component design, but much more complex iLogic.

    So once we have this parameterized model, how do we then create the process for scaling it up to each individual person? So we created a custom application that uses our Factory Four API. The scan is input into our system. We created a WebGL visualizer that creates a local version of these frames that the user is able to interact with. So they're able to go through a series of changes that are based on some anatomical markers.

    So these are the anatomical markers that you can see here. We take the pupils, we take the edges of what we call the face width, and as well as the nose. And using these, we're able to give them a pretty wide degree of freedom to customize the design in a way that aesthetically appeals to them and in a way that they feel connected to. So you can get the perfectly fitting frame that is optically accurate as well.

    So from this very approximated WebGL implementation, we're able to understand the set of 33 parameters that go to inform that one particular design for that person. And now, leveraging the Forge system, and leveraging the Inventor engine, and the Design Automation API in particular, we're able to take those 33 parameters and utilize Forge to be able to render high quality STL.

    Now, had we not had Forge, we would have had to write our own tessellation engine to be able to generate the high enough quality STLs for these very delicate hinges and frames to be printed. And that's kind of impossible for a team of undergraduate students. We knew some web application development. We definitely didn't have the in-depth knowledge of tessellation and how to go from a design to a model that can be printed. Inventor has been doing this for years and years and years. And now we're able to rely on the credibility and the accuracy of the Inventor engine to be able to scale up this process to our company's expectations.

    So we're also doing similar work in the orthotics and prosthetics space, going from a scan of a foot to an orthotic. And the dental industry is another industry that we've been actually working in, to go from implants, and organizing them into an assembly, and then rendering them for production. So that really wouldn't have been possible without the Forge engine and without the Inventor engine in particular.

    NATALIA POLIKARPOVA: Thank you, Alex. Thank you. That's really good. Now, the second project, Johannes Braumann, University for Arts and Design in Linz.

    JOHANNES BRAUMANN: Something is not working out here. Never move. Awesome. So thank you very much. Let's just quickly change to my project over here. So yeah. My name is Johannes Braumann. I am professor at the University for Arts and Design, Linz, and one of the co-founders of the Association for Robots in Architecture.

    So we started working with-- I mean, I'm an architect by education, actually. But we started working with robots nine years ago. And when we started working with robots, we very quickly realized that the software interfaces are not flexible enough for our purposes. So it basically forced us to become from users of software to developers of software.

    So the result of this was a software called KUKA|prc that's, for example, now running in Autodesk Dynamo and allows us to very quickly and efficiently create tool paths for robot to simulate robot. And it's now being used at companies like Boeing, Adidas, and large construction companies.

    As a platform, we then also founded the Association for Robots in Architecture as a network to promote this kind of robotic processes for our partners and for the creative industry, as well. And one of the challenges for us has still been how we can actually reach the end customer with robotic processes. Because while the interfaces that we've been developing are accessible if you think about robotic software-- you think about creative people who already know how to use visual programming like Autodesk Dynamo, like Grasshopper-- it was still very hard to kind of reach the end customer for these kind of processes.

    And so actually, this is then where Autodesk Forge came in. So for our Forge project, we chose a design or a startup that came out of my university's lab that's called, Print a Drink. So that was developed by Benjamin Greimel. And he had the idea of printing liquid within liquid. So I'm just going to very briefly show you how this approximately works.

    So he developed this from scratch within one semester. And he's actually now patented it and selling it to companies as a marketing tool. So the idea that you get your customized drink. You shape inside a cocktail that you can actually drink. And until now, this has been really mostly something static. So you had a couple of shapes to choose from, and you told whoever was working with the robot, I want that shape or that shape. But we were thinking about how can we do this a bit more flexible, how can we do this parametric, how can we allow people to customize that?

    And, as you can imagine, if this is like an event with alcoholic beverages, you maybe don't want to put your own laptop in front of drunk people. So the idea was really first to think about, how can we make this accessible with the devices that people already have-- mostly their phones. So we started looking into web technologies that would allow us to do that.

    And because we're used to working from a CAD environment, of course Forge was first something we very quickly looked into, because we could then continue working with the CAD files with the processes that we would already have. And we hadn't really done anything with web technology before, if you don't count something like, I don't know-- how do I maximize that? Is that maximized? Yeah. So we maybe worked with WordPress or something simple. So for us, it was quite interesting to look into web technologies.

    And with Forge, with the tutorials we were actually able to very quickly get something going. So in this case, for example, you can then zoom in, create your own patterns, or that you can also use predefined shapes, like for example, here a helix, and so on. So this is all quite basic Forge.

    However, what's tricky for us was to think about how can we actually get the robots into the picture. Because the robot is a big part of the entertainment factor. This is kind of how you sell the project. So we also wanted to have the robot moving in the software. So that was first where we started thinking, how can we actually do it. Does it make sense to take our entire logic that was now written in .NET and then move it over to JavaScript so we have it running in the browser?

    And actually for us, the answer was then that we actually decided just to keep the logic in .NET as we have it, and moved it into Microsoft Azure cloud. So we have it running in Microsoft Azure, and we have it accessible from a REST API. So the same software that's driving the robot from Autodesk Dynamo is now also [INAUDIBLE] through the clouds and allows you to access it and simulate the robot.

    So here, after you've put your points in here, you can actually just move the slide. I give a little bit of time, depending on how fast the internet is. And then you can simulate and test the entire process and see how and if it's working. So I mean, in this case, as you can imagine, this is for visualization. So this is not necessarily something that you need as a feedback. But for more complex parts, you could also see, can the robot reach that position? Is it even possible to do that or not?

    And the next thing we had to tackle in that case was that we thought about how can we actually bring that to the machine. Because we had the problem that our cloud is basically infinitely scalable. We can have 100 people using the Forge cloud and the Microsoft Azure cloud at the same time. But then we only have one robot, for example.

    So in this case, you can then go ahead and, for example, in this case just say, order-- I don't know how to start it. Yeah. I just enter something random. No, that's actually-- email address is here. I'm sorry. That's me using a Mac. I'm just typing in random stuff. Basically, you order it. It gets sent up into blob storage. And actually, you then get a QR code back, which apparently maybe I messed it up somehow with the escape.

    Anyway, because this is then the idea. You can then move to the robot, you scan your QR code. It gets the codes, the tool path, back out from the cloud storage and then manufactures your custom robot.

    So yeah. For us, the Forge was a really nice and accessible way to very quickly visualize data in a nice and beautiful way, and to link it with our existing logic. And I think it kind of shows the potential of opening up robotics also to the end customer. So the Print a Drink startup is like for us the very first step to look into that. And of course, this can then be scaled up to a very large scale fabrication and other purposes, as well.

    NATALIA POLIKARPOVA: Thank you, Johannes. That was cool, right?

    [APPLAUSE]

    OK. Now Alexander Braun, Technical University of Munich.

    ALEXANDER BRAUN: All right. So welcome, everybody. My name is Alex Braun. And I am from the Technical University of Munich. And I am a researcher at the Chair of Computational Modeling and Simulation. And the chair is part of a research center called the Leonhard Obermeyer Center. And we have currently around 60 researchers who are focusing on the digitalization of the build environment. So we have several chairs that are, for example, focusing on photogrammetry, or geoinformatics, and also relevant topics like computational modeling.

    And one of the research projects we did, which is also my project and was, we called it Progress Track. And it focused on automation tracking of construction processes. And in this project, we had several different methods we tried to track the current status of a construction site.

    And well, what we did was in the end, we landed with UAV drones and flew around the construction site, took lots of pictures, and then generated a point cloud out of these pictures, and afterwards did an as-planned/as-built comparison with the actual BIM model, which of course needed to be a 4D BIM model-- so not only holding the geometric information, but also semantic information like in this case, the process plan and the time schedule of the complete project.

    And what you can see here is one of the construction sites we monitored. Right now my mouse does not work. Can we reload the page? Let's see. OK. It's working. So it's one of the construction sites. And as you can see, it loaded quite fast. The viewer is very basic. It's the Forge web viewer based on the Model Derivative API. And we enhance it with additional features like, for example, a complete element list of all construction elements that are in this building, and also some additional information that I'm coming to right now, for example, the progress chart, Gantt chart of the construction site.

    And what we see here are the monitoring of the observations on different timestamps. So we did an as-planned/as-built comparison, uploaded the data via the Data Management API to our server. And now what we have here is a full web viewer of the building. And we can select the status of one observation and see which elements were detected, which elements were not detected, and maybe which elements are delayed so that we can now say, OK, we are at this point of our construction progress, but some elements were not detected, so therefore, we might be behind our schedule, and therefore we are delayed. And this is, of course, very important information for construction workers, that they know, where are we with our project? Are ahead or behind schedule?

    And what we can do with this view is very impressive, I think. So you can filter all your elements, basically see, OK, these elements have this current status. And well, it's a very good way to visualize the current status. We also have several construction projects with large construction companies in Germany.

    And as you can see, here is another project of a big sports company in Germany. They are building a new headquarter. And this is only one of the floors they are building. It's a very large steel construction. And well, I think it's very impressive to view this. I think there are around 50,000 steel parts visible in this web viewer. You can very fast rotate it and see it. And that's very impressive for me, that you can bring this information to the construction site on a mobile device, on any device. Well, the end user uses it without installing any additional software.

    And that's it from my side. Thank you very much.

    [APPLAUSE]

    NATALIA POLIKARPOVA: Thank you, Alex. And one more project, Jinyue Zhang from Tianjin University in China.

    JINYUE ZHANG: Thank you, Natalia. Hello, everyone. My name is Jinyue Zhang. I'm an Associate Professor in the Department of Civil Engineering-- actually, sorry-- Civil Construction Management. I'm also holding the [? adjuncture ?] at the University of Toronto. That's in Department of Civil Engineering. So my research area in general is IT application in construction. And recently I have been focusing on the use of BIM in virtual design of the construction.

    And so I'm a-- it's not this one? Oh, yeah. Yeah. So I'm also in charge of a research lab at Tianjin university. The lab is called the Tianjin University Trimble Joint Research Lab for BIM. From the name you know the partner is Trimble. So we use a lot of hardware, like laser scanners, robotic total stations. So about early this year, we launched three student projects using Forge, either as a supporting platform for facility management, or as a tool for data exchange.

    So this is one of the three projects. In this project, we used Forge as a tool for data exchange. We integrated the Revit model into Hololens for facility management. So with this kind of integration, we are able to superimpose the model information which are natively created in Revit into reality builds.

    The reason we use Forge is because Revit is the most popular BIM authoring tool. And Unity is the most popular virtual reality engine. But unfortunately, the two do not work very well with each other. And if you load a well-defined Revit model into Unity Project, a lot of the information may get lost, for example, the material settings, the light settings.

    And with Forge, we solved this problem perfectly. So we upload the Revit model into Forge, and then download an offline data package. And then we write a piece of code to transfer this offline data package into Unity. So we got all information natively created in Revit perfectly showing in the Hololens.

    So this is the video. So how the Hololens got the information is we actually have the Revit model into the Unity Project, then we developed some interactive features. And this also can be controlled by voice. So just forward it to--

    So after you mount the Hololens, this is how you'll see the reality. And you can just show the air ducts and show the air supply. So all the hidden components in the ceiling will be shown up here by its digital counterpart created in Revit model. And also, you can hide the supply air ducts.

    And the next feature we develop is how we show the information related to those components, also by voice command, along with some gestures. We're stuck there? Yeah, maybe the wireless connection. OK. Here.

    So after you show the hidden components in reality view, you can use those blue dots to highlight a component which is selected. And then you can use voice command to say, like, show information. Yeah, connection is very slow. And you can see it's highlighted by the glowing blue color. Yeah. Probably we need to get it downloaded before.

    Yeah. See, after you say, show information. I'm sorry, it's in Chinese character. It's a student project. You can't demand too much. And so this is the early stage of this Forge project. And we are still working on some other features. And a few days ago, we just got another feature like the measuring done. And you can select two points in the augmented view. And so you can measure the distance there.

    So yeah, that's it. Thank you very much.

    [APPLAUSE]

    NATALIA POLIKARPOVA: OK. Thank you very much. Now you can take a seat. So choose which ones you would like. And we're going to ask you a few questions. And the first one would be to Alex Mathews. So tell us how you got started with it. Why Forge, what challenges-- you kind of mentioned it, right? But yeah.

    ALEX MATHEWS: Should I start?

    NATALIA POLIKARPOVA: No, no. Just whatever you want.

    ALEX MATHEWS: Yeah. Yeah, how we got started with Forge is we had begun in the orthotics and prosthetics basically creating our own ankle for the orthotic device.

    The problem we were running into is we had a beautiful model in the CAD environment, but we had trouble translating that into something that the average clinician would be able to use. So Forge allowed us to take a very complex model and keep it on the server side, almost hidden, in a black box, but then expose certain parameters out to clinicians and create a more interactive and more user-friendly type of experience that would allow them to interact with a very complex model.

    You saw something similar in the keynote today with LimbForge. And I think the new IDX platform will really make that process a lot easier than it was for us. But that will allow a much simpler user interface, very complex CAD engines, and allow you to achieve really high results with the outputs you get from your models.

    NATALIA POLIKARPOVA: Right. Thank you. Jinyue, you've been working with Forge for like three years now, right?

    JINYUE ZHANG: Yeah. Almost three years. At that time, it's called Large Model Viewer.

    NATALIA POLIKARPOVA: Right. So how did you get started? So what was interesting about that?

    JINYUE ZHANG: The reason we went to use Forge is because we are in the construction industry and I'm in the construction management part. And for the construction management, we want to consume the BIM model created in the design and the construction further beyond the construction and design phases to the operation phase.

    And at that time-- like back to three years ago and before the Large Model Viewer or Forge is available-- in the industry, what they did is they do the secondary development either on top of Revit or on top of Navisworks. And they developed some facility management functionalities. But the two issues are, first, this is the standalone application-- it's not in cloud-- and people are not able to access the information from a remote location or mobile devices; the second is you have to buy Revit lessons or Navisworks license for that reason-- that could be a good thing for Autodesk-- but a lot of owners and the facility managers asked, why do I have to buy design software in order to use facility management applications?

    So this is the reason we go to Forge. And with Forge, the two issues are solved perfectly. It's got cloud, and also the viewer is free.

    NATALIA POLIKARPOVA: All right. And with you two, I'm going to the second question. So you kind of mentioned what challenges you were trying to address. I would ask you, what next? What is the next step for you? What is the next way for you to go?

    ALEXANDER BRAUN: Well, for me-- yeah, what are the challenge-- well, we tried to address the challenge. We want to bring our resize of the construction monitoring to the construction site. And the big problem is, onsite, you normally don't have large laptops, probably not with full OS, so only a mobile device. And how can we bring some viewer to the site that is well scalable, and fast, and that can visualize large amounts of data?

    Because what we realized is, when we have large models, like the second model I showed, you get really big problems viewing them on mobile devices. And it's really fascinating that Forge is capable of doing this. And for us, it is, well, basically the best idea to get it onsite to visualize the data.

    JOHANNES BRAUMANN: Yeah. I mean, for us, what's next is a good question. So first, Print a Drink was really a prototype. So this is, I would say, impressive, but still, from robot and modeling-wise, a comparably simple application. And we could have a testbed to really explore how these kind of technologies mesh together. And really starting from not knowing anything, really, about web technology, so at first it was a very steep learning curve, not necessarily everything on the Forge side, but also in other cloud technology side.

    So we had really much to learn. And we now have this at a stage where it's working, actually very fine and reliable. So for us the next step is definitely going to be how to apply this in a more complex environment. How can we then really solve more complex manufacturing problems that, at the moment, are maybe not yet addressed.

    But for us, the important part is we have now the framework running. And we can also, for example, look into integrating this kind of robotic technology through the cloud, for example, into Fusion and similar software.

    NATALIA POLIKARPOVA: All right. Thank you. So Alex, your next step with the project.

    ALEX MATHEWS: So for us, we're actually planning on implementing our use of the Design Automation API for some of our other clients. So I think one of the more interesting cases is the automotive industry. We're working with a large automotive manufacturer to automatically customize the tooling for end effectors for robots-- so for body panels that are very complex shapes, a very similar problem where we give them a very simple user interface that allows them to interact with a complex model and then create those very beautifully tessellated and well-structured models that we're able to 3D print in an effective way.

    I think the eyewear example is a great proof-of-concept. It was actually a little bit less challenging in comparison to some of the other problems we're currently working on in research and development. So I think we're excited to see in what other industries and what other use cases we can take our same [INAUDIBLE].

    NATALIA POLIKARPOVA: All right. Thank you. So how about Toronto University? What are they going to join us?

    JINYUE ZHANG: Yeah, so we are exploring more application points for using Forge. And one more idea we are going to do is to use Forge along with Stingray, which is the Autodesk VR engine. And so we want to get the Revit model, then go to the Forge, then go to the Stingray, to be applied into some VR devices so people can simulate construction methods. So this is our next step.

    NATALIA POLIKARPOVA: OK. So I know there was a question, but we'll come back to that at the end of the session, OK? This question about the benefits of the cloud and everything, when we were thinking about that-- so normally, people go from desktop to the cloud. Here, we have, Alex was born in the cloud. He can't imagine going to-- but Alexander, you are going both ways, right?

    ALEXANDER BRAUN: Yeah.

    NATALIA POLIKARPOVA: So tell us about that.

    ALEXANDER BRAUN: Yeah, well, actually for me, I started programming in school and had my own small company basically doing web development. So I started with PHP, JavaScript development. Later on during my studies, I stayed civil engineering, and with focus on informatics a little bit. And then I really got deep into a C#, .NET development.

    So I stayed, basically, on the .NET development side, because I felt I had no real applications in civil engineering that I could get done online and in the browser. So .NET was a really good way to solve the programming problems that I needed to tackle. So this was my current step.

    And then actually, right now I'm getting back with Forge, since it enables me to get my project into the cloud.

    NATALIA POLIKARPOVA: Interesting. So it's like both-- both directions. Johannes, what about you? So cloud benefits-- so what's that for you, for your project?

    JOHANNES BRAUMANN: Yeah, I mean, I talked about this anyways. Of course, this is, just like with LimbForge, it's a way of making more complex technology accessible, and also, that Alex showed. So this is, of course, one part. What's also now getting interesting with, we talked about in which way the data flows, is of course, the other way around, as well. So we use also Forge, for example, visualizing the movement of an actual robot in real time. So you can check on the machine, you can query certain properties of the robot. So this is definitely also something that we're looking into.

    And in many of these applications, to be entirely honest, it's not 100% important that you actually see the robot moving in 3D. So you could make a fancy home page with-- or non-fancy home page, a simple home page, with six number values that keep on changing. But I think we're all very visually inclined. And we're now starting to kind of expect a more complex visualization to get a feeling for certain values for certain properties.

    So for this, I think the cloud, and then by extension, with all the three-dimensional visualization with Forge, is a very good tool.

    NATALIA POLIKARPOVA: OK. Thank you. Now, the next question I'm going to ask the audience-- you. You've heard these wonderful people talking about their projects. So which three words do you think they would use to describe Forge, if you're just limited to three? And then you'll hear them and see who is a winner. So what three words do you think they would use to describe Forge and how it works? Any ideas? Yes?

    AUDIENCE: Web.

    NATALIA POLIKARPOVA: Web. OK. What else? OK. It's time to wake up a little bit.

    AUDIENCE: Platform.

    NATALIA POLIKARPOVA: OK. Anything else?

    AUDIENCE: Collaboration.

    NATALIA POLIKARPOVA: Collaboration. Perfect. All right. OK. Let's hear the panelists. Jinyue, so which three words would you use?

    JINYUE ZHANG: "Cloud." Because it supports the work on mobile. Because for the construction management, especially the facility management, a lot of workers are mobile. And the second is the "platform." And actually, from the research point of view, it's allowed students from different application directions so they can work together and to have new ideas. And the third is, like that gentleman said, the "collaboration." I would say "interoperable" or "interoperability." And it's allowed the software that was not able to work together now to work together.

    NATALIA POLIKARPOVA: All right. Alex?

    ALEX MATHEWS: Yeah, so I actually had in mind a phrase, a three-word phrase for this question. But I think the phrase was "build to build." So Forge allows you the building plugs that you need to empower other people that may not have the full understanding of CAD or full understanding of the power of these engines to be able to build things that would have required years of training. So for example, our CAD engineers are able to build all of these complex shapes in the Inventor environment, spent hours doing it. But now we're allowing the average consumer to build that on the road.

    NATALIA POLIKARPOVA: Build to build. OK. Interesting. Alexander?

    ALEXANDER BRAUN: Yeah. Well, I go with adjectives. And I would choose and "fast," "scalable," and "responsive." So basically "responsive" in the web developer's point of view meaning responsive design. So for every end user's item-- so you can use laptop, you can use a tablet, you can use a smartphone. Even if I use my smartphone right now showing the same model, it has the same performance. And well, that's my words.

    NATALIA POLIKARPOVA: OK. Johannes?

    JOHANNES BRAUMANN: Yeah, being the last one, of course, out of your three words at least two are already taken. So maybe I just add. This might be a bit I'm not so computer science-y or whatever. But something that's been really important for me is that it's beautiful. Because it has a really nice way of showing geometry.

    So what you saw, for example, when the robot move, also in the other cases when you saw about the transparency in Alex's projects, and the others. So it's really impressive that you upload geometry and it immediately looks good-- and not in this kind of 1980s look, CAD look, but really with proper shading, shadows, and all that. And I think this is very important, because it's just essentially information that geometry is presented in the right way. And in this case, just out of the box, without having to adjust many things, you get something that you look at and it looks modern, and it looks like something you can work with.

    And for me, this was probably one of the reasons why we stuck with Forge. Because we got it to work, it looked good, and so we decided that it's worth the effort of looking into the further details to make it not only look good but also work good. Because of course, there's a difference. But still, it's a big motivation to us to see something and think, yeah, let's do that.

    NATALIA POLIKARPOVA: OK. So before we open to the questions from the audience, anything you would like to ask each other? Jinyue, you have a question, right?

    JINYUE ZHANG: Yeah. So my question is for Alexander. You showed the 4D simulation to compare with the design model, to compare which component is late, right? And so you use drone to scan the construction site. How often do you scan that?

    ALEXANDER BRAUN: Well, we had different projects with different monitoring times. We currently stuck with once weekly, because actually, computing takes some time, and it's always a point of the data management. You get a lot of data when you fly every day or twice a day or every hour. You get so much data you need to process.

    And in our research, we found out that it's sufficient for most projects, at least in the European construction sites, where the construction sites are very focused on in situ concrete-- so not that much prefabricated items. And there is a very huge focus on weekly-based processes. So it's also we have some processes that use lean management. And they are also focusing on one-week cycles. And this is basically the reason we also focused on one-week cycles.

    JINYUE ZHANG: Yeah. So if weekly, it's acceptable. Otherwise, if you require daily, you will be hated by a construction manager.

    ALEXANDER BRAUN: Exactly.

    NATALIA POLIKARPOVA: Johannes, do you have any questions for the co-speakers?

    JOHANNES BRAUMANN: I'm actually looking forward to questions from the audience.

    NATALIA POLIKARPOVA: OK. All right. Let's do that. So questions.

    AUDIENCE: Sorry. I know it's kind of heresy, it being a Forge conference. But did you guys evaluate any other platforms or systems, softwares prior to choosing Forge that compared in any way to Forge in the features and the problems that you were trying to solve?

    JOHANNES BRAUMANN: Well, we didn't really have a huge choice, I would say. I mean, from the 3D engine point of view, what Forge is also built on top is Three.js, which is of course working well in the web, but it's missing the kind of additional CAD features that then Forge puts on top of it, basically. Otherwise, of course the choice would have been game engines like Unity.

    But as far as I know, I'm not sure if Unity WebGL is properly working already or if this is still under development. But so we looked into several possibilities, but then ended up with Forge, because the kind of feature set that we needed was kind of workflow from CAD. Involving CAD operations wasn't really available anywhere else.

    ALEXANDER BRAUN: We actually tried developing the viewer by ourselves also using Three.js. And in general, it worked, but we had a lot of pre-processing work to do. So we needed to triangulate the model by ourselves. In the beginning, we just had the model all in one color with no material information, no shading, nothing at all. And then it looks really crappy.

    And that's also one point we chose Forge, because it looks really good. And we can easily include our IFC data, Revit data, anything we want. And it looks good. We have the material information. We have our [INAUDIBLE], which we can use to reference any additional data we want to include. And well, it has very, very lower work from our side, from the developing point of side. So we definitely chose Ford for it.

    ALEX MATHEWS: Yeah. So for us, for the viewer our client had very specific requirements for the visualization. I mean, we had to put a head in there. So that was a little bit more difficult. We actually wrote--

    NATALIA POLIKARPOVA: So you didn't use our [INAUDIBLE].

    ALEX MATHEWS: We did not use [INAUDIBLE]. But we didn't have any other options. So we did write our own, using Three.js. It was the only other option, really. But when it came to the Design Automation API, before really learning about Forge and Fusion, we actually used SolidWorks. So that's kind of heresy. But once we started learning about the tools that Autodesk had, we actually completely switched over to using Inventor and using the Design Automation API.

    Because there's no other service that allows you to interact with a CAD engine in the way that Autodesk is now allowing us to interact with using the Forge platform. It's just not possible. The type of high scalability you can get from running these jobs and the Design Automation API is unparalleled.

    NATALIA POLIKARPOVA: That's Design Automation for Inventor, right?

    ALEX MATHEWS: Correct. Yes. For Inventor.

    NATALIA POLIKARPOVA: Jinyue?

    JINYUE ZHANG: Yeah. Honestly, we tried the Unity before we get to know of Forge. And with the Unity, this kind of game engine, and the pain is that you have to rebuild the model. Just like I said, the Revit just doesn't work with Unity. And creating the model in 3ds Max is not a big problem. But the real problem is we accumulated a lot of information from the design and construction phase.

    And if you want to use the Unity to create the facility management functionality, you have to re-enter those data to populate the data into your new 3ds Max model. This is a big pain, and takes up too much time. So far, we have this kind of Forge as a data exchange tool. We can either use Forge alone as the platform for facility management. Or if the client or project owner wants highly rendered views, we can move to the Unity.

    NATALIA POLIKARPOVA: OK. Thank you. Does that answer your question?

    AUDIENCE: I have a kind of follow-up question [INAUDIBLE]--

    NATALIA POLIKARPOVA: OK.

    AUDIENCE: So as [INAUDIBLE] so I assume that [INAUDIBLE] for each of these applications [INAUDIBLE] will eventually be [INAUDIBLE] some industries. How do you see Forge pricing impacting your ability to use Forge in the future. So Forge [INAUDIBLE] just be actually selling your service or the core function [INAUDIBLE]?

    NATALIA POLIKARPOVA: Did you consider pricing already?

    ALEX MATHEWS: Yeah. So fortunately, because we're an early tester of the Inventor we get it for free now.

    NATALIA POLIKARPOVA: Right.

    ALEX MATHEWS: So I think-- yeah, so we're a completely independent company from Johns Hopkins. So now we're kind of fully functioning on our own. We've raised a round of funding, and Forge is definitely a big part of our business plan. I don't know if I should be too honest, but it is quite cheap. It really is of very little concern. You can kind of consider the--

    NATALIA POLIKARPOVA: So your investors are happy with the business planning that you [INAUDIBLE].

    ALEX MATHEWS: Yes. They're happy. They're content. But you can kind of look at it almost like cloud computing. There is a cost associated to it, but it is so cheap that usually the things that you're selling, like our software services and our professional services that go into the engineering of these platforms, that cost is usually significantly higher than the cost you actually pay for the infrastructure. So you can almost look at Forge like infrastructure, like the cloud, like AWS or Google Cloud.

    NATALIA POLIKARPOVA: Interesting. So Jinyue, when you develop with the students, do you actually pay attention to these things, like kind of business planning, anything?

    JINYUE ZHANG: No, actually, as a research institute, we got it for free.

    NATALIA POLIKARPOVA: I mean, in general, when you prepare them to kind of going into the world.

    JINYUE ZHANG: Yeah, you know what? Actually, at the university, business part is not a big concern for us. But speaking for business, and I think for the facility management part, if you are developing a facility management application, as long as the viewer is free and the development part is not big cost for the business model, this is my point of view.

    NATALIA POLIKARPOVA: OK.

    JOHANNES BRAUMANN: So I just always find it cute when Microsoft sends us this 5 euro invoices for Asia. So I haven't really looked into the new Forge credits yet. But if they're on that scale, it should be doable.

    ALEXANDER BRAUN: I think so, too. So when you consider the rest of, in my case, the monitoring part, the calculation, everything, this is a very minor cost. So I don't think it would be a problem.

    NATALIA POLIKARPOVA: Any other questions? Whoa, good. Please. You're the next.

    AUDIENCE: [INAUDIBLE]

    NATALIA POLIKARPOVA: Oh, can I share something before the-- that guy actually got a nomination from Forbes 30 under 30. Can you imagine that?

    AUDIENCE: Congratulations. [INAUDIBLE]

    NATALIA POLIKARPOVA: Wow. Yay.

    AUDIENCE: So I just want to [INAUDIBLE] the question that if you listen to the keynotes, everybody emphasizes that with Forge, that initial workflow is changing. So how do you see that industry starting to get more collaborative in their research? Because the way I see it, you come from research-- either you thought to do something or you start your own business. Did you see any other opportunity [INAUDIBLE] industry and did you look at that [INAUDIBLE]?

    NATALIA POLIKARPOVA: Something to think about.

    ALEXANDER BRAUN: Well, for us, I think we are already pretty strong with engaging with industry. Because we have our research center, which has a lot of companies that engage with us. And they are all partners at our research center.

    And basically, it's one of the reasons my research project could be so successful, because we had so many construction companies that provided the construction site that we needed to get the data for the research project. So in my case, I think it's already working very good. But that's, I think, a very local issue, what we have at Technical University of Munich, that I have this opportunity using this.

    But I think it would be very good for companies to go to universities, to the specific chairs at the universities, that deal with, in our case, the BIM philosophies, and talk to them about the ongoing research project, for starters.

    NATALIA POLIKARPOVA: All right. Johannes, anything to add?

    JOHANNES BRAUMANN: Not so much, actually. I mean, for us the research is based, actually, really very much on industry. So the software we're developing is developing out of projects, actual projects, that are being done with our partners. They have problems that need to be solved, and then we develop solutions for that. So the university part, also how I came to this presentation, is really that the startup from Benjamin, Print a Drink, came out of university.

    But the software, while it's being applied at university, a big focus is definitely the actual applications that then really span from people buying a 4,000 euro robot on eBay and using it to fabricate boats-- this really happened-- and then to high-end timber manufacturers having mighty 200,000 euro setups to fabricate 200 square meter wood panels. So this stretches quite a wide area then. So it's not a homogeneous area, but it's quite interesting, with many different requirements.

    NATALIA POLIKARPOVA: And we only have time for one question left.

    AUDIENCE: It's more directed towards the operations side. So as these workloads are changing, where you start using Forge and you start leaving people behind, one of the struggles that you always find is, who won't fit in? How do you facilitate that user [INAUDIBLE]. Because one of the issues is you can give the owner as much [INAUDIBLE] data that you want, but not in terms of [INAUDIBLE] just sits on a flash drive or something [? like that. ?] How do you get the owners to utilize it, getting them smart enough to understand it, so in 20 years from now, the owner is not calling your organization to figure it out?

    JINYUE ZHANG: Yeah. So that's a very good question, actually. And who owns the data is a big issue. Even results, thinking about the operation phase, even in the design construction phase, when the collaboratively create a model from different disciplines, who owns the data? That's a big question. And there is no perfect answer so far. And I can tell you that in China, the practice is basically who paid for that, who own the data.

    So at this time, normally it's the project owner demanding that data. So they paid for creating the model. And so they own the model, they own the data. And for the long run of the operation, so there is actually reaching up kind of business. It's the model management. And some companies, especially the design firm, they extend their business scope beyond the traditional design, and they go to the model management. Because they believe that some owners, they are not able to manage those models in the long run. Maybe some part is modified or something. And they help you to manage the data.

    NATALIA POLIKARPOVA: OK. Now, for those of you who are new-- and there were quite a lot of hands-- we have the Answer Bar at AU, if you're staying at AU, the whole week. So make sure you really come, you learn as much as possible. And we hope our speakers today got you excited. Maybe you have some interesting ideas. So come to us. Share with us. And we're ready to be with you on this journey.

    Thank you very much for joining us today.

    [APPLAUSE]

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    Geo Targetly
    我们使用 Geo Targetly 将网站访问者引导至最合适的网页并/或根据他们的位置提供量身定制的内容。 Geo Targetly 使用网站访问者的 IP 地址确定访问者设备的大致位置。 这有助于确保访问者以其(最有可能的)本地语言浏览内容。Geo Targetly 隐私政策
    SpeedCurve
    我们使用 SpeedCurve 来监控和衡量您的网站体验的性能,具体因素为网页加载时间以及后续元素(如图像、脚本和文本)的响应能力。SpeedCurve 隐私政策
    Qualified
    Qualified is the Autodesk Live Chat agent platform. This platform provides services to allow our customers to communicate in real-time with Autodesk support. We may collect unique ID for specific browser sessions during a chat. Qualified Privacy Policy

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    改善您的体验 – 使我们能够为您展示与您相关的内容

    Google Optimize
    我们通过 Google Optimize 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Google Optimize 隐私政策
    ClickTale
    我们通过 ClickTale 更好地了解您可能会在站点的哪些方面遇到困难。我们通过会话记录来帮助了解您与站点的交互方式,包括页面上的各种元素。将隐藏可能会识别个人身份的信息,而不会收集此信息。. ClickTale 隐私政策
    OneSignal
    我们通过 OneSignal 在 OneSignal 提供支持的站点上投放数字广告。根据 OneSignal 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 OneSignal 收集的与您相关的数据相整合。我们利用发送给 OneSignal 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. OneSignal 隐私政策
    Optimizely
    我们通过 Optimizely 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Optimizely 隐私政策
    Amplitude
    我们通过 Amplitude 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Amplitude 隐私政策
    Snowplow
    我们通过 Snowplow 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Snowplow 隐私政策
    UserVoice
    我们通过 UserVoice 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. UserVoice 隐私政策
    Clearbit
    Clearbit 允许实时数据扩充,为客户提供个性化且相关的体验。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。Clearbit 隐私政策
    YouTube
    YouTube 是一个视频共享平台,允许用户在我们的网站上查看和共享嵌入视频。YouTube 提供关于视频性能的观看指标。 YouTube 隐私政策

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    定制您的广告 – 允许我们为您提供针对性的广告

    Adobe Analytics
    我们通过 Adobe Analytics 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Adobe Analytics 隐私政策
    Google Analytics (Web Analytics)
    我们通过 Google Analytics (Web Analytics) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Web Analytics) 隐私政策
    AdWords
    我们通过 AdWords 在 AdWords 提供支持的站点上投放数字广告。根据 AdWords 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AdWords 收集的与您相关的数据相整合。我们利用发送给 AdWords 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AdWords 隐私政策
    Marketo
    我们通过 Marketo 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。我们可能会将此数据与从其他信息源收集的数据相整合,以根据高级分析处理方法向您提供改进的销售体验或客户服务体验以及更相关的内容。. Marketo 隐私政策
    Doubleclick
    我们通过 Doubleclick 在 Doubleclick 提供支持的站点上投放数字广告。根据 Doubleclick 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Doubleclick 收集的与您相关的数据相整合。我们利用发送给 Doubleclick 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Doubleclick 隐私政策
    HubSpot
    我们通过 HubSpot 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。. HubSpot 隐私政策
    Twitter
    我们通过 Twitter 在 Twitter 提供支持的站点上投放数字广告。根据 Twitter 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Twitter 收集的与您相关的数据相整合。我们利用发送给 Twitter 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Twitter 隐私政策
    Facebook
    我们通过 Facebook 在 Facebook 提供支持的站点上投放数字广告。根据 Facebook 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Facebook 收集的与您相关的数据相整合。我们利用发送给 Facebook 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Facebook 隐私政策
    LinkedIn
    我们通过 LinkedIn 在 LinkedIn 提供支持的站点上投放数字广告。根据 LinkedIn 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 LinkedIn 收集的与您相关的数据相整合。我们利用发送给 LinkedIn 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. LinkedIn 隐私政策
    Yahoo! Japan
    我们通过 Yahoo! Japan 在 Yahoo! Japan 提供支持的站点上投放数字广告。根据 Yahoo! Japan 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Yahoo! Japan 收集的与您相关的数据相整合。我们利用发送给 Yahoo! Japan 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Yahoo! Japan 隐私政策
    Naver
    我们通过 Naver 在 Naver 提供支持的站点上投放数字广告。根据 Naver 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Naver 收集的与您相关的数据相整合。我们利用发送给 Naver 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Naver 隐私政策
    Quantcast
    我们通过 Quantcast 在 Quantcast 提供支持的站点上投放数字广告。根据 Quantcast 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Quantcast 收集的与您相关的数据相整合。我们利用发送给 Quantcast 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Quantcast 隐私政策
    Call Tracking
    我们通过 Call Tracking 为推广活动提供专属的电话号码。从而,使您可以更快地联系我们的支持人员并帮助我们更精确地评估我们的表现。我们可能会通过提供的电话号码收集与您在站点中的活动相关的数据。. Call Tracking 隐私政策
    Wunderkind
    我们通过 Wunderkind 在 Wunderkind 提供支持的站点上投放数字广告。根据 Wunderkind 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Wunderkind 收集的与您相关的数据相整合。我们利用发送给 Wunderkind 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Wunderkind 隐私政策
    ADC Media
    我们通过 ADC Media 在 ADC Media 提供支持的站点上投放数字广告。根据 ADC Media 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 ADC Media 收集的与您相关的数据相整合。我们利用发送给 ADC Media 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. ADC Media 隐私政策
    AgrantSEM
    我们通过 AgrantSEM 在 AgrantSEM 提供支持的站点上投放数字广告。根据 AgrantSEM 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AgrantSEM 收集的与您相关的数据相整合。我们利用发送给 AgrantSEM 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AgrantSEM 隐私政策
    Bidtellect
    我们通过 Bidtellect 在 Bidtellect 提供支持的站点上投放数字广告。根据 Bidtellect 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bidtellect 收集的与您相关的数据相整合。我们利用发送给 Bidtellect 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bidtellect 隐私政策
    Bing
    我们通过 Bing 在 Bing 提供支持的站点上投放数字广告。根据 Bing 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bing 收集的与您相关的数据相整合。我们利用发送给 Bing 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bing 隐私政策
    G2Crowd
    我们通过 G2Crowd 在 G2Crowd 提供支持的站点上投放数字广告。根据 G2Crowd 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 G2Crowd 收集的与您相关的数据相整合。我们利用发送给 G2Crowd 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. G2Crowd 隐私政策
    NMPI Display
    我们通过 NMPI Display 在 NMPI Display 提供支持的站点上投放数字广告。根据 NMPI Display 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 NMPI Display 收集的与您相关的数据相整合。我们利用发送给 NMPI Display 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. NMPI Display 隐私政策
    VK
    我们通过 VK 在 VK 提供支持的站点上投放数字广告。根据 VK 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 VK 收集的与您相关的数据相整合。我们利用发送给 VK 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. VK 隐私政策
    Adobe Target
    我们通过 Adobe Target 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Adobe Target 隐私政策
    Google Analytics (Advertising)
    我们通过 Google Analytics (Advertising) 在 Google Analytics (Advertising) 提供支持的站点上投放数字广告。根据 Google Analytics (Advertising) 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Google Analytics (Advertising) 收集的与您相关的数据相整合。我们利用发送给 Google Analytics (Advertising) 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Google Analytics (Advertising) 隐私政策
    Trendkite
    我们通过 Trendkite 在 Trendkite 提供支持的站点上投放数字广告。根据 Trendkite 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Trendkite 收集的与您相关的数据相整合。我们利用发送给 Trendkite 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Trendkite 隐私政策
    Hotjar
    我们通过 Hotjar 在 Hotjar 提供支持的站点上投放数字广告。根据 Hotjar 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Hotjar 收集的与您相关的数据相整合。我们利用发送给 Hotjar 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Hotjar 隐私政策
    6 Sense
    我们通过 6 Sense 在 6 Sense 提供支持的站点上投放数字广告。根据 6 Sense 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 6 Sense 收集的与您相关的数据相整合。我们利用发送给 6 Sense 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. 6 Sense 隐私政策
    Terminus
    我们通过 Terminus 在 Terminus 提供支持的站点上投放数字广告。根据 Terminus 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Terminus 收集的与您相关的数据相整合。我们利用发送给 Terminus 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Terminus 隐私政策
    StackAdapt
    我们通过 StackAdapt 在 StackAdapt 提供支持的站点上投放数字广告。根据 StackAdapt 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 StackAdapt 收集的与您相关的数据相整合。我们利用发送给 StackAdapt 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. StackAdapt 隐私政策
    The Trade Desk
    我们通过 The Trade Desk 在 The Trade Desk 提供支持的站点上投放数字广告。根据 The Trade Desk 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 The Trade Desk 收集的与您相关的数据相整合。我们利用发送给 The Trade Desk 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. The Trade Desk 隐私政策
    RollWorks
    We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

    是否确定要简化联机体验?

    我们希望您能够从我们这里获得良好体验。对于上一屏幕中的类别,如果选择“是”,我们将收集并使用您的数据以自定义您的体验并为您构建更好的应用程序。您可以访问我们的“隐私声明”,根据需要更改您的设置。

    个性化您的体验,选择由您来做。

    我们重视隐私权。我们收集的数据可以帮助我们了解您对我们产品的使用情况、您可能感兴趣的信息以及我们可以在哪些方面做出改善以使您与 Autodesk 的沟通更为顺畅。

    我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

    通过管理您在此站点的隐私设置来了解个性化体验的好处,或访问我们的隐私声明详细了解您的可用选项。