[](https://github.com/nerfstudio-project/nerfacc/actions/workflows/code_checks.yml) [](https://github.com/nerfstudio-project/nerfacc/actions/workflows/doc.yml) [](https://pepy.tech/project/nerfacc) https://www.nerfacc.com/ [News] 2023/04/04. If you were using `nerfacc <= 0.3.5` and would like to migrate to our latest version (`nerfacc >= 0.5.0`), Please check the [CHANGELOG](CHANGELOG.md) on how to migrate. NerfAcc is a PyTorch Nerf acceleration toolbox for both training and inference. It focus on efficient sampling in the volumetric rendering pipeline of radiance fields, which is universal and plug-and-play for most of the NeRFs. With minimal modifications to the existing codebases, Nerfacc provides significant speedups in training various recent NeRF papers. **And it is pure Python interface with flexible APIs!**  ## Installation **Dependence**: Please install [Pytorch](https://pytorch.org/get-started/locally/) first. The easist way is to install from PyPI. In this way it will build the CUDA code **on the first run** (JIT). ``` pip install nerfacc ``` Or install from source. In this way it will build the CUDA code during installation. ``` pip install git+https://github.com/nerfstudio-project/nerfacc.git ``` We also provide pre-built wheels covering major combinations of Pytorch + CUDA supported by [official Pytorch](https://pytorch.org/get-started/previous-versions/). ``` # e.g., torch 1.13.0 + cu117 pip install nerfacc -f https://nerfacc-bucket.s3.us-west-2.amazonaws.com/whl/torch-1.13.0_cu117.html ``` | Windows & Linux | `cu113` | `cu115` | `cu116` | `cu117` | `cu118` | |-----------------|---------|---------|---------|---------|---------| | torch 1.11.0 | ✅ | ✅ | | | | | torch 1.12.0 | ✅ | | ✅ | | | | torch 1.13.0 | | | ✅ | ✅ | | | torch 2.0.0 | | | | ✅ | ✅ | For previous version of nerfacc, please check [here](https://nerfacc-bucket.s3.us-west-2.amazonaws.com/whl/index.html) on the supported pre-built wheels. ## Usage The idea of NerfAcc is to perform efficient volumetric sampling with a computationally cheap estimator to discover surfaces. So NerfAcc can work with any user-defined radiance field. To plug the NerfAcc rendering pipeline into your code and enjoy the acceleration, you only need to define two functions with your radience field. - `sigma_fn`: Compute density at each sample. It will be used by the estimator (e.g., `nerfacc.OccGridEstimator`, `nerfacc.PropNetEstimator`) to discover surfaces. - `rgb_sigma_fn`: Compute color and density at each sample. It will be used by `nerfacc.rendering` to conduct differentiable volumetric rendering. This function will receive gradients to update your radiance field. An simple example is like this: ``` python import torch from torch import Tensor import nerfacc radiance_field = ... # network: a NeRF model rays_o: Tensor = ... # ray origins. (n_rays, 3) rays_d: Tensor = ... # ray normalized directions. (n_rays, 3) optimizer = ... # optimizer estimator = nerfacc.OccGridEstimator(...) def sigma_fn( t_starts: Tensor, t_ends:Tensor, ray_indices: Tensor ) -> Tensor: """ Define how to query density for the estimator.""" t_origins = rays_o[ray_indices] # (n_samples, 3) t_dirs = rays_d[ray_indices] # (n_samples, 3) positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 sigmas = radiance_field.query_density(positions) return sigmas # (n_samples,) def rgb_sigma_fn( t_starts: Tensor, t_ends: Tensor, ray_indices: Tensor ) -> Tuple[Tensor, Tensor]: """ Query rgb and density values from a user-defined radiance field. """ t_origins = rays_o[ray_indices] # (n_samples, 3) t_dirs = rays_d[ray_indices] # (n_samples, 3) positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 rgbs, sigmas = radiance_field(positions, condition=t_dirs) return rgbs, sigmas # (n_samples, 3), (n_samples,) # Efficient Raymarching: # ray_indices: (n_samples,). t_starts: (n_samples,). t_ends: (n_samples,). ray_indices, t_starts, t_ends = estimator.sampling( rays_o, rays_d, sigma_fn=sigma_fn, near_plane=0.2, far_plane=1.0, early_stop_eps=1e-4, alpha_thre=1e-2, ) # Differentiable Volumetric Rendering. # colors: (n_rays, 3). opaicity: (n_rays, 1). depth: (n_rays, 1). color, opacity, depth, extras = nerfacc.rendering( t_starts, t_ends, ray_indices, n_rays=rays_o.shape[0], rgb_sigma_fn=rgb_sigma_fn ) # Optimize: Both the network and rays will receive gradients optimizer.zero_grad() loss = F.mse_loss(color, color_gt) loss.backward() optimizer.step() ``` ## Examples: Before running those example scripts, please check the script about which dataset is needed, and download the dataset first. You could use `--data_root` to specify the path. ```bash # clone the repo with submodules. git clone --recursive git://github.com/nerfstudio-project/nerfacc/ ``` ### Static NeRFs See full benchmarking here: https://www.nerfacc.com/en/stable/examples/static.html Instant-NGP on NeRF-Synthetic dataset with better performance in 4.5 minutes. ``` bash # Occupancy Grid Estimator python examples/train_ngp_nerf_occ.py --scene lego --data_root data/nerf_synthetic # Proposal Net Estimator python examples/train_ngp_nerf_prop.py --scene lego --data_root data/nerf_synthetic ``` Instant-NGP on Mip-NeRF 360 dataset with better performance in 5 minutes. ``` bash # Occupancy Grid Estimator python examples/train_ngp_nerf_occ.py --scene garden --data_root data/360_v2 # Proposal Net Estimator python examples/train_ngp_nerf_prop.py --scene garden --data_root data/360_v2 ``` Vanilla MLP NeRF on NeRF-Synthetic dataset in an hour. ``` bash # Occupancy Grid Estimator python examples/train_mlp_nerf.py --scene lego --data_root data/nerf_synthetic ``` TensoRF on Tanks&Temple and NeRF-Synthetic datasets (plugin in the official codebase). ``` bash cd benchmarks/tensorf/ # (set up the environment for that repo) bash script.sh nerfsyn-nerfacc-occgrid 0 bash script.sh tt-nerfacc-occgrid 0 ``` ### Dynamic NeRFs See full benchmarking here: https://www.nerfacc.com/en/stable/examples/dynamic.html T-NeRF on D-NeRF dataset in an hour. ``` bash # Occupancy Grid Estimator python examples/train_mlp_tnerf.py --scene lego --data_root data/dnerf ``` K-Planes on D-NeRF dataset (plugin in the official codebase). ```bash cd benchmarks/kplanes/ # (set up the environment for that repo) bash script.sh dnerf-nerfacc-occgrid 0 ``` TiNeuVox on HyperNeRF and D-NeRF datasets (plugin in the official codebase). ```bash cd benchmarks/tineuvox/ # (set up the environment for that repo) bash script.sh dnerf-nerfacc-occgrid 0 bash script.sh hypernerf-nerfacc-occgrid 0 bash script.sh hypernerf-nerfacc-propnet 0 ``` ### Camera Optimization NeRFs See full benchmarking here: https://www.nerfacc.com/en/stable/examples/camera.html BARF on the NeRF-Synthetic dataset (plugin in the official codebase). ```bash cd benchmarks/barf/ # (set up the environment for that repo) bash script.sh nerfsyn-nerfacc-occgrid 0 ``` ### 3rd-Party Usages: #### Awesome Codebases. - [nerfstudio](https://github.com/nerfstudio-project/nerfstudio): A collaboration friendly studio for NeRFs. - [sdfstudio](https://autonomousvision.github.io/sdfstudio/): A unified framework for surface reconstruction. - [threestudio](https://github.com/threestudio-project/threestudio): A unified framework for 3D content creation. - [instant-nsr-pl](https://github.com/bennyguo/instant-nsr-pl): NeuS in 10 minutes. - [modelscope](https://github.com/modelscope/modelscope/blob/master/modelscope/models/cv/nerf_recon_acc/network/nerf.py): A collection of deep-learning algorithms. #### Awesome Papers. - [Representing Volumetric Videos as Dynamic MLP Maps, CVPR 2023](https://github.com/zju3dv/mlp_maps) - [NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads, ArXiv 2023](https://tobias-kirschstein.github.io/nersemble/) - [HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion, ArXiv 2023](https://synthesiaresearch.github.io/humanrf/) ## Common Installation Issues

ImportError: .../csrc.so: undefined symbol

If you are installing a pre-built wheel, make sure the Pytorch and CUDA version matchs with the nerfacc version (nerfacc.__version__).

## Citation ```bibtex @article{li2023nerfacc, title={NerfAcc: Efficient Sampling Accelerates NeRFs.}, author={Li, Ruilong and Gao, Hang and Tancik, Matthew and Kanazawa, Angjoo}, journal={arXiv preprint arXiv:2305.04966}, year={2023} } ```