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…
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…
Outdoor Scene Extrapolation with Hierarchical Generative Cellular Automata
Dongsu Zhang, Francis Williams, Zan Gojcic +4
We aim to generate fine-grained 3D geometry from large-scale sparse LiDAR scans, abundantly captured by autonomous vehicles (AV). Contrary to prior work on AV scene completion, we…
NeRF-XL: Scaling NeRFs with Multiple GPUs
Ruilong Li, Sanja Fidler, Angjoo Kanazawa +1
We present NeRF-XL, a principled method for distributing Neural Radiance Fields (NeRFs) across multiple GPUs, thus enabling the training and rendering of NeRFs with an arbitrarily…