Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Thomas Müller, Alex Evans, Christoph Schied, Alexander Keller
1/16/2022
Keywords: Speed & Computational Efficiency, Fundamentals, Generalization, Hybrid Geometry Representation, Positional Encoding
Venue: ARXIV 2022
Bibtex:
@article{muller2022instantngp,
author = {Thomas Muller and Alex Evans and Christoph Schied and Alexander Keller},
title = {Instant Neural Graphics Primitives with a Multiresolution Hash Encoding},
year = {2022},
month = {Jan},
url = {http://arxiv.org/abs/2201.05989v1}
}
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 ${1920\!\times\!1080}$.
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