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Instant-NGP recently introduced a Multi-resolution Hash Encoding for neural graphics primitives like NeRFs. The original NVIDIA implementation mainly in C++/CUDA, based on tiny-cuda-nn, can train NeRFs upto 100x faster!
This project is a pure PyTorch implementation of Instant-NGP, built with the purpose of enabling AI Researchers to play around and innovate further upon this method.
This project is built on top of the super-useful NeRF-pytorch implementation.
HashNeRF-pytorch (left) vs NeRF-pytorch (right):
Chair.Convergence.mp4
After training for just 5k iterations (~10 minutes on a single 1050Ti), you start seeing a crisp chair rendering. :)
Download the nerf-synthetic dataset from here: Google Drive.
To train a chair HashNeRF model:
python run_nerf.py --config configs/chair.txt --finest_res 512 --log2_hashmap_size 19 --lrate 0.01 --lrate_decay 10
To train for other objects like ficus/hotdog, replace configs/chair.txt with configs/{object}.txt:
The code-base has additional support for:
- Total Variation Loss for smoother embeddings (use
--tv-loss-weightto enable) - Sparsity-inducing loss on the ray weights (use
--sparse-loss-weightto enable)
The repo now supports training a NeRF model on a scene from the ScanNet dataset. I personally found setting up the ScanNet dataset to be a bit tricky. Please find some instructions/notes in ScanNet.md.
- Voxel pruning during training and/or inference
- Accelerated ray tracing, early ray termination
Kudos to Thomas Müller and the NVIDIA team for this amazing work, that will greatly help accelerate Neural Graphics research:
@article{mueller2022instant,
title = {Instant Neural Graphics Primitives with a Multiresolution Hash Encoding},
author = {Thomas M\"uller and Alex Evans and Christoph Schied and Alexander Keller},
journal = {arXiv:2201.05989},
year = {2022},
month = jan
}
Also, thanks to Yen-Chen Lin for the super-useful NeRF-pytorch:
@misc{lin2020nerfpytorch,
title={NeRF-pytorch},
author={Yen-Chen, Lin},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\url{https://github.com/yenchenlin/nerf-pytorch/}},
year={2020}
}
If you find this project useful, please consider to cite:
@misc{bhalgat2022hashnerfpytorch,
title={HashNeRF-pytorch},
author={Yash Bhalgat},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\url{https://github.com/yashbhalgat/HashNeRF-pytorch/}},
year={2022}
}