[](https://github.com/KAIR-BAIR/nerfacc/actions/workflows/code_checks.yml) [](https://www.nerfacc.com/en/latest/?badge=latest) [](https://pepy.tech/project/nerfacc) https://www.nerfacc.com/ NerfAcc is a PyTorch Nerf acceleration toolbox for both training and inference. It focus on efficient volumetric rendering of radiance fields, which is universal and plug-and-play for most of the NeRFs. Using NerfAcc, - The `vanilla NeRF` model with 8-layer MLPs can be trained to *better quality* (+~0.5 PNSR) in *1 hour* rather than *days* as in the paper. - The `Instant-NGP NeRF` model can be trained to *equal quality* in *4.5 minutes*, comparing to the official pure-CUDA implementation. - The `D-NeRF` model for *dynamic* objects can also be trained in *1 hour* rather than *2 days* as in the paper, and with *better quality* (+~2.5 PSNR). - Both *bounded* and *unbounded* scenes are supported. **And it is pure Python interface with flexible APIs!** ## Installation ``` pip install nerfacc ``` ## Usage The idea of NerfAcc is to perform efficient ray marching and volumetric rendering. 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 `nerfacc.ray_marching()` to skip the empty and occluded space during ray marching, which is where the major speedup comes from. - `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 network. 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 def sigma_fn( t_starts: Tensor, t_ends:Tensor, ray_indices: Tensor ) -> Tensor: """ Query density values from a user-defined radiance field. :params t_starts: Start of the sample interval along the ray. (n_samples, 1). :params t_ends: End of the sample interval along the ray. (n_samples, 1). :params ray_indices: Ray indices that each sample belongs to. (n_samples,). :returns The post-activation density values. (n_samples, 1). """ 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) / 2.0 sigmas = radiance_field.query_density(positions) return sigmas # (n_samples, 1) 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. :params t_starts: Start of the sample interval along the ray. (n_samples, 1). :params t_ends: End of the sample interval along the ray. (n_samples, 1). :params ray_indices: Ray indices that each sample belongs to. (n_samples,). :returns The post-activation rgb and density values. (n_samples, 3), (n_samples, 1). """ 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) / 2.0 rgbs, sigmas = radiance_field(positions, condition=t_dirs) return rgbs, sigmas # (n_samples, 3), (n_samples, 1) # Efficient Raymarching: Skip empty and occluded space, pack samples from all rays. # packed_info: (n_rays, 2). t_starts: (n_samples, 1). t_ends: (n_samples, 1). with torch.no_grad(): packed_info, t_starts, t_ends = nerfacc.ray_marching( 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 = nerfacc.rendering(rgb_sigma_fn, packed_info, t_starts, t_ends) # 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 it is needed, and download the dataset first. ``` bash # Instant-NGP NeRF in 4.5 minutes with reproduced performance! # See results at here: https://www.nerfacc.com/en/latest/examples/ngp.html python examples/train_ngp_nerf.py --train_split train --scene lego ``` ``` bash # Vanilla MLP NeRF in 1 hour with better performance! # See results at here: https://www.nerfacc.com/en/latest/examples/vanilla.html python examples/train_mlp_nerf.py --train_split train --scene lego ``` ```bash # D-NeRF for Dynamic objects in 1 hour with better performance! # See results at here: https://www.nerfacc.com/en/latest/examples/dnerf.html python examples/train_mlp_dnerf.py --train_split train --scene lego ``` ```bash # Instant-NGP on unbounded scenes in 20 minutes! # See results at here: https://www.nerfacc.com/en/latest/examples/unbounded.html python examples/train_ngp_nerf.py --train_split train --scene garden --auto_aabb --unbounded --cone_angle=0.004 ``` ## Citation ```bibtex @article{li2022nerfacc, title={NerfAcc: A General NeRF Accleration Toolbox.}, author={Li, Ruilong and Tancik, Matthew and Kanazawa, Angjoo}, journal={arXiv preprint arXiv:2210.04847}, year={2022} } ```