We introduce GVDB Sparse Volumes as a new offering with NVIDIA DesignWorks to focus on high quality raytracing of sparse volumetric data for motion pictures. Based on the VDB topology of Museth, with a novel GPU-based data structure and API, GVDB is designed for efficient compute and raytracing on a sparse hierarchy of grids. Raytracing on the GPU is accelerated with indexed memory pooling, 3D texture atlas storage and a new hierarchical traversal algorithm. GVDB integrates with NVIDIA OptiX, and is developed as an open source library as a part of DesignWorks.
We present a novel technique for visualization of scientific data with compute operators and multi-scatter ray tracing entirely on GPU. Our source data consists of a high-resolution simulation using point-based wavelets, a representation not supported by existing tools. To visualize this data, and consider dynamic time-based rendering, our approach is inspired by OpenVDB from motion pictures, which uses a hierarchy of grids similar to AMR. We develop GVDB, a ground-up implementation with tree traversal, compute, and ray tracing via OptiX all on the GPU. GVDB enables multi-scatter rendering at 200 million rays/sec, and full-volume compute operations in a few milliseconds on datasets up to 4,200^3 entirely in GPU memory.
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.