Add references to instant NGP

DOCUMENTATION.md

@@ -118,7 +118,8 @@ The number of encoded dimensions is twice the specified number of frequencies fo
 
 ### Grid
 
-Encoding based on trainable multiresolution grids. The grids can be backed by hashtables, dense storage, or tiled storage.
+Encoding based on trainable multiresolution grids.
+Used for [Instant Neural Graphics Primitives [Müller et al. 2022]](https://nvlabs.github.io/instant-ngp/). The grids can be backed by hashtables, dense storage, or tiled storage.
 
 The number of encoded dimensions is `n_levels * n_features_per_level`.
 

README.md

@@ -29,17 +29,20 @@ _Fully fused networks vs. TensorFlow v2.5.0 w/ XLA. Measured on 64 (solid line)
 
 This framework powers the following publications:
 
+> __Instant Neural Graphics Primitives with a Multiresolution Hash Encoding__ 
+> [Thomas Müller](https://tom94.net), [Alex Evans](https://research.nvidia.com/person/alex-evans), [Christoph Schied](https://research.nvidia.com/person/christoph-schied), [Alexander Keller](https://research.nvidia.com/person/alex-keller) 
+> _arXiv [cs.GR], Jan 2022_ 
+> __[ [Website](https://nvlabs.github.io/instant-ngp/) ] [ [Paper](https://nvlabs.github.io/instant-ngp/assets/mueller2022instant.pdf) ] [ [Code](https://github.com/NVlabs/instant-ngp) ] [ [Video](https://nvlabs.github.io/instant-ngp/assets/mueller2022instant.mp4) ]__
+
 > __Real-time Neural Radiance Caching for Path Tracing__ 
 > [Thomas Müller](https://tom94.net), [Fabrice Rousselle](https://research.nvidia.com/person/fabrice-rousselle), [Jan Novák](http://jannovak.info), [Alexander Keller](https://research.nvidia.com/person/alex-keller) 
-> _ACM Transactions on Graphics (Proceedings of SIGGRAPH), vol. 40, no. 4, pp. 36:1–36:16, Aug 2021_
->
-> [ [Paper](https://tom94.net/data/publications/mueller21realtime/mueller21realtime.pdf) ] [ [GTC talk](https://gtc21.event.nvidia.com/media/Fully%20Fused%20Neural%20Network%20for%20Radiance%20Caching%20in%20Real%20Time%20Rendering%20%5BE31307%5D/1_liqy6k1c) ] [ [Video](https://tom94.net/data/publications/mueller21realtime/mueller21realtime.mp4) ] [ [Interactive Results Viewer](https://tom94.net/data/publications/mueller21realtime/interactive-viewer/) ] [ [BibTeX](https://tom94.net/data/publications/mueller21realtime/mueller21realtime.bib) ]
+> _ACM Transactions on Graphics (Proceedings of SIGGRAPH), vol. 40, no. 4, pp. 36:1–36:16, Aug 2021_ 
+> __[ [Paper](https://tom94.net/data/publications/mueller21realtime/mueller21realtime.pdf) ] [ [GTC talk](https://gtc21.event.nvidia.com/media/Fully%20Fused%20Neural%20Network%20for%20Radiance%20Caching%20in%20Real%20Time%20Rendering%20%5BE31307%5D/1_liqy6k1c) ] [ [Video](https://tom94.net/data/publications/mueller21realtime/mueller21realtime.mp4) ] [ [Interactive Results Viewer](https://tom94.net/data/publications/mueller21realtime/interactive-viewer/) ] [ [BibTeX](https://tom94.net/data/publications/mueller21realtime/mueller21realtime.bib) ]__
 
 > __Extracting Triangular 3D Models, Materials, and Lighting From Images__ 
 > [Jacob Munkberg](https://research.nvidia.com/person/jacob-munkberg), [Jon Hasselgren](https://research.nvidia.com/person/jon-hasselgren), [Tianchang Shen](http://www.cs.toronto.edu/~shenti11/), [Jun Gao](http://www.cs.toronto.edu/~jungao/), [Wenzheng Chen](http://www.cs.toronto.edu/~wenzheng/), [Alex Evans](https://research.nvidia.com/person/alex-evans), [Thomas Müller](https://tom94.net), [Sanja Fidler](https://www.cs.toronto.edu/~fidler/) 
-> _[arXiv:2111.12503 [cs.CV]](https://arxiv.org/abs/2111.12503)_, Nov 2021
->
-> [ [Website](https://nvlabs.github.io/nvdiffrec/) ] [ [Paper](https://nvlabs.github.io/nvdiffrec/assets/paper.pdf) ] [ [Video](https://nvlabs.github.io/nvdiffrec/assets/video.mp4) ] [ [BibTeX](https://nvlabs.github.io/nvdiffrec/assets/bib.txt) ]
+> _[arXiv:2111.12503 [cs.CV]](https://arxiv.org/abs/2111.12503)_, Nov 2021 
+> __[ [Website](https://nvlabs.github.io/nvdiffrec/) ] [ [Paper](https://nvlabs.github.io/nvdiffrec/assets/paper.pdf) ] [ [Video](https://nvlabs.github.io/nvdiffrec/assets/video.mp4) ] [ [BibTeX](https://nvlabs.github.io/nvdiffrec/assets/bib.txt) ]__
 
 ## Usage
 
@@ -155,7 +158,7 @@ The following is a summary of all components of this framework that are currentl
 | :--- | :---------- | :-----
 | Composite | `include/tiny-cuda-nn/encodings/composite.h` | Allows composing multiple encodings. Can be, for example, used to assemble the Neural Radiance Caching encoding [[Müller et al. 2021]](https://tom94.net/).
 | Frequency | `include/tiny-cuda-nn/encodings/frequency.h` | NeRF's [[Mildenhall et al. 2020]](https://www.matthewtancik.com/nerf) positional encoding applied equally to all dimensions.
-| Grid | `include/tiny-cuda-nn/encodings/grid.h` | Encoding based on trainable multiresolution grids. The grids can be backed by hashtables, dense storage, or tiled storage.
+| Grid | `include/tiny-cuda-nn/encodings/grid.h` | Encoding based on trainable multiresolution grids. Used for [Instant Neural Graphics Primitives [Müller et al. 2022]](https://nvlabs.github.io/instant-ngp/). The grids can be backed by hashtables, dense storage, or tiled storage.
 | Identity | `include/tiny-cuda-nn/encodings/identity.h` | Leaves values untouched.
 | Oneblob | `include/tiny-cuda-nn/encodings/oneblob.h` | From Neural Importance Sampling [[Müller et al. 2019]](https://tom94.net/data/publications/mueller18neural/mueller18neural-v4.pdf) and Neural Control Variates [[Müller et al. 2020]](https://tom94.net/data/publications/mueller20neural/mueller20neural.pdf).
 | SphericalHarmonics | `include/tiny-cuda-nn/encodings/spherical_harmonics.h` | A frequency-space encoding that is more suitable to direction vectors than component-wise ones.