We'll explore NVIDIA GVDB Voxels, a new open source SDK framework for generic representation, computation, and rendering of voxel-based data. We'll introduce the features of the new SDK and cover applications and examples in motion pictures, scientific visualization, and 3D printing. NVIDIA GVDB Voxels, based on GVDB Sparse Volume technology and inspired by OpenVDB, manipulates large volumetric datasets entirely on the GPU using a hierarchy of grids. The second part of the talk will cover in-depth use of the SDK, with code samples, and coverage of the design aspects of NVIDIA GVDB Voxels. A sample code walk-through will demonstrate how to build sparse volumes, render high-quality images with NVIDIA OptiX integration, produce dynamic data, and perform compute-based operations.
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.