Lossy Geometry Compression for High Resolution Voxel Scenes

Abstract

Sparse Voxel Directed Acyclic Graphs (SVDAGs) losslessly compress highly detailed geometry in a high-resolution binary voxel grid by identifying matching elements. This representation is suitable for high-performance real-time applications, such as free-viewpoint videos and high-resolution precomputed shadows. In this work, we introduce a lossy scheme to further decrease memory consumption by minimally modifying the underlying voxel grid to increase matches. Our method efficiently identifies groups of similar but rare subtrees in an SVDAG structure and replaces them with a single common subtree representative. We test our compression strategy on several standard voxel datasets, where we obtain memory reductions of 10% up to 50% compared to a standard SVDAG, while introducing an error (ratio of modified voxels to voxel count) of only 1% to 5%. Furthermore, we show that our method is complementary to other state of the art SVDAG optimizations, and has a negligible effect on real-time rendering performance.

Editing Compressed High‐resolution Voxel Scenes with Attributes

“…First expressed by [WD89] in two dimensions, it was later extended to 3D by [PU03] and popularised by [KSA13]. Extensions to this approach match identical subtrees under symmetry [VMG16; ČMBB19] or allow approximate merges [vdLSE20].…”

CSVO: Clustered Sparse Voxel Octrees—A Hierarchical Data Structure for Geometry Representation of Voxelized 3D Scenes

“…While the aforementioned works focused on lossless compression of scene geometries using hierarchical data structures, attention was also given to lossy compression. In 2020, van der Laan et al introduced Lossy Sparse Voxel Directed Acyclic Graphs (LSVDAGs), based on SVDAGs [21]. In its construction process, not only absolutely identical subtrees were searched for, but also more rarely occurring subtrees that only required minimal modification to become identical.…”