“Instant neural graphics primitives with a multiresolution hash encoding” (2022), by Thomas Müller et al. Computer Science research paper. Original abstract below, unchanged. A free PDF is available.

Instant neural graphics primitives with a multiresolution hash encoding

Thomas Müller, Alex Evans, Christoph Schied et al. · July 1, 2022 · Computer Science

Abstract

Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations: a small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through stochastic gradient descent. The multiresolution structure allows the network to disambiguate hash collisions, making for a simple architecture that is trivial to parallelize on modern GPUs. We leverage this parallelism by implementing the whole system using fully-fused CUDA kernels with a focus on minimizing wasted bandwidth and compute operations. We achieve a combined speedup of several orders of magnitude, enabling training of high-quality neural graphics primitives in a matter of seconds, and rendering in tens of milliseconds at a resolution of 1920×1080.

Free PDF DOI: 10.1145/3528223.3530127