Compression and rendering of voxelized 3D scene representations
With the increase in performance and programmability of graphical processing units (GPUs), GPU raycasting is emerging as an efficient solution for many real-time rendering problems in a variety of application domains, from industrial CAD rendering to gaming to cultural heritage data presentation.
In order to handle large detailed scenes, devising a compact and efficient scene representation for accelerating ray-geometry intersection queries becomes paramount and many solutions have been proposed.
Among these, sparse voxel octrees (SVO) have provided impressive results, since they can be created from a variety of scene representation, they efficiently carve out empty space, with benefits on ray tracing performance and memory needs and they implicitly provide a levels-of-detail (LOD) mechanism.
Given their still relatively high memory cost and the required high memory bandwidth, these voxelized approaches have, however, been limited to moderate scene sizes and resolutions, or to effects that do not require precise geometric details.
It is therefore important to devise novel scene representations that can provide compact representations within reasonable memory footprints, while not requiring decompression overhead.
CRS4 has created and developed methods for efficient lossless compression of voxelized geometry in a format that keeps the same visibility-query performance. Our approach profits from redundancy and symmetry present in common scenes by merging subtrees that are identical through a similarity transform. Moreover, we exploit the skewed distribution of references to shared nodes to store child pointers using a variable bit-rate encoding. We have also shown how, by selecting plane reflections along the main grid directions as symmetry transforms, we can construct highly compressed GPU-friendly structures using a fully out-of-core method. Our results demonstrate that state-of-the-art compression and real-time tracing performance can be achieved on high-resolution voxelized representations of real-world scenes of very different characteristics, including large CAD models, 3D scans, and typical gaming models. The method has been implemented in an open-source software that covers compression and rendering (SSVDAG).
- state-of-the-art compression performance of sparse voxel octrees using advanced deduplication and pointer compression;
- exploitation of symmetries in a GPU friendly structure;
- complete solutions for scalable processing and rendering of voxelized representation starting from massive triangulated surface models.
Researchers in visual computing, engineers active in industrial settings and gaming.
ICT - Cultural Heritage - Gaming.
- SSVDAG framework
- Alberto Jaspe Villanueva. Scalable Exploration of 3D Massive Models. PhD thesis. PhD Programme in Information and Communications Technology, University of A Coruña, Spain, 2018.
- Alberto Jaspe Villanueva, Fabio Marton, and Enrico Gobbetti. Symmetry-aware Sparse Voxel DAGs (SSVDAGs) for compression-domain tracing of high-resolution geometric scenes. Journal of Computer Graphics Techniques, 6(2): 1-30, 2017.
- Alberto Jaspe Villanueva, Fabio Marton, and Enrico Gobbetti. SSVDAGs: Symmetry-aware Sparse Voxel DAGs. In Proc. ACM i3D. Pages 7-14, February 2016.