Instant Neural Graphics Primitives with a Multiresolution Hash Encoding

• URL: http://arxiv.org/abs/2201.05989v1
• Date: Sun, 16 Jan 2022 07:22:47 GMT
• Title: Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
• Authors: Thomas M\"uller, Alex Evans, Christoph Schied, Alexander Keller
• Abstract summary: We present a versatile new input encoding that permits the use of a smaller network without sacrificing quality. A small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through a gradient descent. We achieve a combined speed of several orders of magnitude, enabling training of high-quality neural graphics primitives in a matter of seconds.
• Score: 67.33850633281803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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