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

Abstract

When representing the geometry of voxelized three-dimensional scenes (especially if they have been voxelized to high resolutions) in a naive—uncompressed—form, one may end up using vast amounts of data. These can easily attack the available memory capacity of the graphics card, the operating memory or even secondary storage of computer. A viable solution to this problem is to use domain-specific hierarchical data structures, based on octant trees or directed acyclic graphs, which, among other advantages, provide a compact binary representation that can thus be considered to be their compressed encoding. These data structures include—inter alia—sparse voxel octrees, sparse voxel directed acyclic graphs and symmetry-aware sparse voxel directed acyclic graphs. The paper deals with the proposal of a new domain-specific hierarchical data structure: the clustered sparse voxel octrees. It is designed to represent the geometry of voxelized three-dimensional scenes and can be constructed using the out-of-core algorithm proposed in the paper. The advantage of the presented data structure is in its compact binary representation, achieved by omitting a significant number of pointers to child nodes (82.55% in case of Angel Lucy model in 1283 voxels resolution) and by using a wider range of child node pointer lengths, including 8b, 16b and 32b. We achieved from 6.57 to 6.82 times more compact encoding, compared to sparse voxel octrees, whose all node components were 32b aligned, and from 4.11 to 4.27 times more compact encoding, when not all node components were 32b aligned.

AHiLS—An Algorithm for Establishing Hierarchy among Detected Weak Local Reflection Symmetries in Raster Images

“…Originally, two subtrees could be merged on the basis of similarity or connectivity, which they extended to the use of reflection symmetries as well. In this manner, the concept of Sparse Voxel Directed Acyclic Graphs (SVDAGs) was upgraded into the Symmetry-Aware Sparse Voxel Directed Acyclic Graphs (SSVDAGs), which were further analysed by Madoš et al [47]. Although all these methods use a similar concept of symmetry-aware hierarchical organisation of spatial data, they were designed for different purposes, and thus, none of them is directly comparable to AHiLS.…”