Plenoxels: Radiance Fields without Neural Networks

Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa

UC Berkeley

Website and video: https://alexyu.net/plenoxels

Note: This is a preliminary release. We have not carefully tested everything, but feel that it would be better to first put the code out there.

Also, despite the name, it’s not strictly intended to be a successor of svox

Citation:

@inproceedings{yu2022plenoxels,
      title={Plenoxels: Radiance Fields without Neural Networks}, 
      author={Sara Fridovich-Keil and Alex Yu and Matthew Tancik and Qinhong Chen and Benjamin Recht and Angjoo Kanazawa},
      year={2022},
      booktitle={CVPR},
}

Note that the joint first-authors decided to swap the order of names between arXiv and CVPR proceedings.

This contains the official optimization code. A JAX implementation is also available at https://github.com/sarafridov/plenoxels. However, note that the JAX version is currently feature-limited, running in about 1 hour per epoch and only supporting bounded scenes (at present).

Fast optimization

Overview

Setup

First create the virtualenv; we recommend using conda:

conda env create -f environment.yml
conda activate plenoxel

Then clone the repo and install the library at the root (svox2), which includes a CUDA extension.

If your CUDA toolkit is older than 11, then you will need to install CUB as follows: conda install -c bottler nvidiacub. Since CUDA 11, CUB is shipped with the toolkit.

To install the main library, simply run

pip install .

In the repo root directory.

Getting datasets

We have backends for NeRF-Blender, LLFF, NSVF, and CO3D dataset formats, and the dataset will be auto-detected.

Please get the NeRF-synthetic and LLFF datasets from: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1 (nerf_synthetic.zip and nerf_llff_data.zip).

We provide a processed Tanks and temples dataset (with background) in NSVF format at: https://drive.google.com/file/d/1PD4oTP4F8jTtpjd_AQjCsL4h8iYFCyvO/view?usp=sharing

Note this data should be identical to that in NeRF++

Finally, the real Lego capture can be downloaded from: https://drive.google.com/file/d/1PG-KllCv4vSRPO7n5lpBjyTjlUyT8Nag/view?usp=sharing

Voxel Optimization (aka Training)

For training a single scene, see opt/opt.py. The launch script makes this easier.

Inside opt/, run ./launch.sh <exp_name> <GPU_id> <data_dir> -c <config>

Where <config> should be configs/syn.json for NeRF-synthetic scenes, configs/llff.json for forward-facing scenes, and configs/tnt.json for tanks and temples scenes, for example.

The dataset format will be auto-detected from data_dir. Checkpoints will be in ckpt/exp_name.

For pretrained checkpoints please see: https://drive.google.com/drive/folders/1SOEJDw8mot7kf5viUK9XryOAmZGe_vvE?usp=sharing

Evaluation

Use opt/render_imgs.py

Usage, (in opt/) python render_imgs.py <CHECKPOINT.npz> <data_dir>

By default this saves all frames, which is very slow. Add --no_imsave to avoid this.

Rendering a spiral

Use opt/render_imgs_circle.py

Usage, (in opt/) python render_imgs_circle.py <CHECKPOINT.npz> <data_dir>

Parallel task executor

We provide a parallel task executor based on the task manager from PlenOctrees to automatically schedule many tasks across sets of scenes or hyperparameters. This is used for evaluation, ablations, and hypertuning See opt/autotune.py. Configs in opt/tasks/*.json

For example, to automatically train and eval all synthetic scenes: you will need to change train_root and data_root in tasks/eval.json, then run:

python autotune.py -g '<space delimited GPU ids>' tasks/eval.json

For forward-facing scenes

python autotune.py -g '<space delimited GPU ids>' tasks/eval_ff.json

For Tanks and Temples scenes

python autotune.py -g '<space delimited GPU ids>' tasks/eval_tnt.json

Using a custom image set (360)

Please take images all around the object and try to take images at different elevations. First make sure you have colmap installed. Then

(in opt/scripts) bash proc_colmap.sh <img_dir>

Where <img_dir> should be a directory directly containing png/jpg images from a normal perspective camera. For custom datasets we adopt a data format similar to that in NSVF https://github.com/facebookresearch/NSVF

You should be able to use this dataset directly afterwards. The format will be auto-detected.

To view the data use: python view_data.py <img_dir>

This should launch a server at localhost:8889

Now follow the “Voxel Optimization (aka Training)” section to train:

./launch.sh <exp_name> <GPU_id> <data_dir> -c configs/custom.json

You can also try configs/custom_alt.json which has some minor differences. You may need to tune the TV for best results.

To render a video, please see the “rendering a spiral” section. To convert to a svox1-compatible PlenOctree (not perfect quality since interpolation is not implemented) you can try to_svox1.py <ckpt>

Random tip: how to make pip install faster for native extensions

You may notice that this CUDA extension takes forever to install. A suggestion is using ninja. On Ubuntu, install it with sudo apt install ninja-build. Then set the environment variable MAX_JOBS to the number of CPUS to use in parallel (e.g. 12) in your shell startup script. This will enable parallel compilation and significantly improve iteration speed.