Dynamic GPU Voxel Grids and Voxel-sequential Processing in NanoVDB

Eftychios Sifakis, Francis Williams, Christopher Horvath, Rohan Sawhney, Mark Harris +2

We introduce two fundamental advances to the NanoVDB data structure: dynamic GPU-native topological updates, and a voxel-sequential access paradigm that dramatically accelerates point-wise and stencil-based operators on sparse grids. NanoVDB's linearized memory layout has delivered strong GPU performance, and the Index-Grid concept introduced in the fVDB framework [Williams et al 2024] provided topology/data separation and optimal compactness of storage in sparse machine learning workloads. However, all these prior iterations of NanoVDB assume a static topology that has precluded use of this data structure in simulation and dynamic geometric processes. We eliminate this limitation by introducing a suite of dynamic topology operators -- morphological dilation, grid merging, pruning, and cross-resolution coarsening and refinement. These operators use bit-parallel algorithms to reconstruct grids containing hundreds of millions of voxels in just a few milliseconds. We also introduce a voxel-sequential access paradigm and data structure for GPU kernels operating on sparse data. We stream active voxels as a flat linear sequence, minimizing or eliminating the need for tree traversal in…