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<div class="container">
<div class="paper-title">
<h1>Instant Neural Graphics Primitives with a Multiresolution Hash Encoding</h1>
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<div class="col-1 text-center">NVIDIA</div>
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<p class="caption_justify">
We demonstrate near-instant training of neural graphics primitives on a single GPU for multiple tasks. In <b>gigapixel image</b> we represent an image by a neural network. <b>SDF</b> learns a signed distance function in 3D space whose zero level-set represents a 2D surface.
- <b>NeRF</b> <a href="https://research.nvidia.com/publication/2021-06_Real-time-Neural-Radiance">[Mildenhall et al. 2020]</a> uses 2D images and their camera poses to reconstruct a volumetric radiance-and-density field that is visualized using ray marching.
+ <b>NeRF</b> <a href="https://www.matthewtancik.com/nerf">[Mildenhall et al. 2020]</a> uses 2D images and their camera poses to reconstruct a volumetric radiance-and-density field that is visualized using ray marching.
Lastly, <b>neural volume</b> learns a denoised radiance and density field directly from a volumetric path tracer.
In all tasks, our encoding and its efficient implementation provide clear benefits: instant training, high quality, and simplicity. Our encoding is task-agnostic: we use the same implementation and hyperparameters across all tasks and only vary the hash table size which trades off quality and performance.
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<h2>News</h2>
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<p class="caption_justify">
- Real-time training progress on the image task where the neural network learns the mapping from 2D coordinates to RGB colors of a high-resolution image. Note that in this video, the network is trained from scratch - but converges so quickly you may miss it if you blink! <br/>
+ Real-time training progress on the image task where the neural network learns the mapping from 2D coordinates to RGB colors of a high-resolution image. Note that in this video, the network is trained from scratch—but converges so quickly you may miss it if you blink!<br/>
</p>
<h3>Neural Radiance Fields</h3>