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2. Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
 
 # Instant Neural Graphics Primitives with a Multiresolution Hash Encoding

  ![](/sites/default/files/styles/wide/public/publications/Screenshot%202022-05-03%20at%2009.52.28.png?itok=rpw6apln)

 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 1920x1080.



 ## Authors



[Thomas Müller](/person/thomas-muller)

Alex Evans (NVIDIA)

Christoph Schied (NVIDIA)

[Alex Keller](/person/alex-keller)

 

 

 ## Publication Date



Sunday, July 24, 2022

 

 ## Published in



[ACM Transactions on Graphics (SIGGRAPH 2022)](https://s2022.siggraph.org)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

[Computational Photography and Imaging](/research-area/computational-photography-imaging)

[Computer Graphics](/research-area/computer-graphics)

[Computer Vision](/research-area/computer-vision)

[High Performance Computing](/research-area/high-performance-computing)

[Real-Time Rendering](/research-area/real-time-rendering)

 

 

 ## External Links



[Project page](https://nvlabs.github.io/instant-ngp)

 

 

 ## Uploaded Files



[Paper](https://d1qx31qr3h6wln.cloudfront.net/publications/mueller2022instant.pdf "Open file in new window")16.87 MB

[2 minute video](https://d1qx31qr3h6wln.cloudfront.net/publications/mueller2022instant_2.mp4 "Open video in new window")10.31 MB

 

 

 ## Awards



Best Technical Paper, SIGGRAPH 2022

THE BEST INVENTIONS OF 2022, TIME

 

 

 ## Copyright



© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.