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Instant Neural Graphics Primitives with a Multiresolution Hash Encoding. (arXiv:2201.05989v2 [cs.CV] UPDATED)
May 5, 2022, 1:12 a.m. | Thomas Müller, Alex Evans, Christoph Schied, Alexander Keller
cs.LG updates on arXiv.org arxiv.org
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 …
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