ARM-HashNeRF-pytorch

This project implements Accelerated Ray Marching (ARM) in HashNerf-pytorch, a pure PyTorch implementation of Instant-NGP. Instant-NGP drastically reduces (up to two orders of magnitude) the cost of training and evaluation of Neural Graphics Primitives that are parametrized by fully connected neural networks.

ARM-HashNeRF vs Vanilla HashNeRF

Both the rendering time and quality of ARM-HashNeRF are compared against Vanilla HashNeRF. In all cases, rendering time is reduced while resulting in only a minimal decrease in redering quality (PSNR). For instance, in the 50K iterations comparison below, ARM-HashNeRF achieves 9.71% faster rendering compared to Vanilla HashNeRF, with only a 5.43% reduction in PSNR. Vanilla HashNeRF is on the left and ARM-HashNeRF on the right. All experiments were run using a single Tesla P100 GPU.

https://github.com/user-attachments/assets/34e75e38-9eb8-4e72-94c4-118879137aee

Contents

• Instructions
• Citation
• Acknowledgments

Instructions

1. Download Dataset

The NeRF synthetic LEGO dataset is used in this project. Please download the preprocessed dataset from here and place it in the ARM-HashNeRF-pytorch/ directory.

2. Clone Repository

git clone git@github.com:jorgedanielrodrividal/ARM-HashNeRF-pytorch.git

3. Install custom vren library

pip install art/csrc/

4. Training

python run_arm_nerf.py --config configs/lego.txt --finest_res 512 --log2_hashmap_size 19 --lrate 0.01 --lrate_decay 10

Citation

This project is mostly based on the amazing work of:

@misc{bhalgat2022hashnerfpytorch, title={HashNeRF-pytorch}, author={Yash Bhalgat}, publisher = {GitHub}, journal = {GitHub repository}, howpublished={\url{https://github.com/yashbhalgat/HashNeRF-pytorch/}}, year={2022} }

@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 }

If you find this work useful, feel free to cite:

@misc{jorgedaniel2025armhashnerfpytorch, title={ARM-HashNeRF-pytorch}, author={Jorge Daniel}, publisher = {GitHub}, journal = {GitHub repository}, howpublished={\url{https://github.com/jorgedanielrodrividal/ARM-HashNeRF-pytorch/}}, year={2025} }

Acknowledgments

Big thanks to Yash Bhalgat for his enlightening HashNeRF project. Also thanks to the author of ngp_pl, which served as a key inspiration for the ARM implementation.