25 citations · 27 across the 4 of their papers we have counts for
4 papers
A Scalable PyTorch Abstraction for Multi-GPU Gaussian Splatting
Matthew Cong, Francis Williams, Jonathan Swartz +3
Gaussian splatting methods have become increasingly popular for neural reconstruction of the real world. However, they are often limited in scale and resolution due to compute and…
Stochastic Barnes-Hut Approximation for Fast Summation on the GPU
Abhishek Madan, Nicholas Sharp, Francis Williams +2
We present a novel stochastic version of the Barnes-Hut approximation. Regarding the level-of-detail (LOD) family of approximations as control variates, we construct an unbiased es…
fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence
Francis Williams, Jiahui Huang, Jonathan Swartz +9
We present fVDB, a novel GPU-optimized framework for deep learning on large-scale 3D data. fVDB provides a complete set of differentiable primitives to build deep learning architec…
XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
Xuanchi Ren, Jiahui Huang, Xiaohui Zeng +3
We present XCube (abbreviated as ), a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model can generate millions of…