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2017 GTC San Jose

S7108 - Sparse Volumetric Representation of Time-Lapse Point Cloud

Session Speakers
Session Description

This talk presents sparse voxelization of time-lapse point cloud. Point cloud has several advantages including capturing easiness, data simplicity, and most fundamental 3D primitive. Because of these advantages, the easy way to collect time-lapse 3D information is by capturing point cloud using laser scan or photogrammetry. However, point cloud representation is lack of spatial connectivity and has notoriously big size of captured data. Our sparse volumetric representation fills the gap between the pros and cons of point cloud by keeping the simplicity and easiness and providing spatial connectivity as well as GPU-friendly data structure. In this talk, we show our massive-scale time-lapse point cloud dataset, the compression as sparse voxels, and further processing in parallel and visualization using GVDB in CUDA.


Additional Session Information
Intermediate
Talk
Large Scale and Multi-Display Visualization Real-Time Graphics Rendering and Ray Tracing
Aerospace Architecture / Engineering / Construction Automotive Defense Games Government / National Labs Higher Education / Research Manufacturing Media & Entertainment
25 minutes
Session Schedule