Instant Neural Graphics Primitives With A Multiresolution Hash Encoding
Müller Thomas, Evans Alex, Schied Christoph, Keller Alexander. ACM Trans. Graph. 2022
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
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