Beschreibung
Terrestrial laser scanning enables accurate capture of complex spaces, such as the interior of factories, hospitals, process plants, and civil infrastructure. Reconstruction of 3D shape and appearance from unmanned aerial vehicle (UAV)-based photographs enables operators to rapidly capture exterior structures and their surroundings. Merging these technologies helps users quickly and accurately capture their entire facility—including impractical areas such as rooftops—and provide outdoor context to indoor scans for complete site mapping. We will present workflows for combining terrestrial scan data and UAV photo models into a unified point cloud for visualization, interrogation, design, modeling, and analysis. We will highlight how operators can capitalize on the accuracy and reliability of laser scanning through UAVs’ capability to quickly capture expansive environments in great detail, and provide reality-captured context to improve downstream workflows in AutoCAD software, Revit software, and InfraWorks software. This session features ReCap 360. AIA Approved
Wichtige Erkenntnisse
- Learn how to align registered, terrestrial laser scans with scaled and aligned photo point clouds using survey control
- Learn how to create scaled and aligned photo point clouds using organic control points from laser scans
- Learn best practices for the collection of laser scan and UAV photos for efficiently creating high-quality interior and exterior point clouds of large industrial and commercial spaces
- Learn about workflows for preparing point clouds for use in design tools such as AutoCAD, Revit, and InfraWorks
Referent
- MZMarc ZinckMarc Zinck is a Software Development Manager at Autodesk in the Emerging Products: ReCap Group leading a team working on the collection, processing, analysis and visualization of captured sensor data. Prior to joining Autodesk, Marc designed, developed and commercialized automated reality capture systems for laser scan workflows. Earlier in his career, as a researcher at the Carnegie Mellon Robotics Institute, he worked on sponsored research projects for DARPA, ARL and NASA building laser based robot navigation and mapping systems for ground and air vehicles. Marc holds a degree in Computer Science from Carnegie Mellon School of Computer Science, has co-authored several peer reviewed publications, is an inventor on patents for geometry synthesis and automated point cloud collection and presents regularly on the topic of reality capture.
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