Abstract
The rapid development of 3D scanning technology combined
with state-of-the-art mapping algorithms allows to
capture 3D point clouds with high resolution and
accuracy. The high amount of data collected with
LiDAR, RGB-D cameras or generated through SfM
approaches makes the direct use of the recorded data
for realistic rendering and simulation
problematic. Therefore, these point clouds have to
be transformed into representations that fulfill the
computational requirements for VR and AR setups. In
this tutorial participants will be introduced to
state-of-the-art methods in point cloud processing
and surface reconstruction with open source software
to learn the benefits for AR and VR applications by
interleaved presentations, software demonstrations
and software trials. The focus lies on 3D point
cloud data structures (range images, octrees, k-d
trees) and algorithms, and their implementation in
C/C++. Surface reconstruction using Marching Cubes
and other meshing methods will play another central
role. Reference material for subtopics like 3D point
cloud registration and SLAM, calibration, filtering,
segmentation, meshing, and large scale surface
reconstruction will be provided. Participants are
invited to bring their Linux, MacOS or Windows
laptops to gain hands-on experience on practical
problems occuring when working with large scale 3D
point clouds in VR and AR applications.
Users
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