Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
This addresses the problem of slow training and evaluation for researchers and practitioners in neural graphics, representing a novel method for a known bottleneck.
The paper tackles the high computational cost of neural graphics primitives by introducing a multiresolution hash encoding that allows using a smaller network without quality loss, achieving speedups of several orders of magnitude, enabling training in seconds and rendering at 1920x1080 resolution in tens of milliseconds.
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 ${1920\!\times\!1080}$.