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:

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.
# ray_indices: (n_samples,). t_starts: (n_samples, 1). t_ends: (n_samples, 1).
with torch.no_grad():
    ray_indices, 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(
    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 it is needed, and download the dataset first.

# clone the repo with submodules.
git clone --recursive git://github.com/KAIR-BAIR/nerfacc/

# 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

# 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

# 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

# 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

Used by:

• nerfstudio: A collaboration friendly studio for NeRFs.
• instant-nsr-pl: NeuS in 10 minutes.

Citation

@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}
}