Awesome
BARF :vomiting_face:: Bundle-Adjusting Neural Radiance Fields
Chen-Hsuan Lin,
Wei-Chiu Ma,
Antonio Torralba,
and Simon Lucey
IEEE International Conference on Computer Vision (ICCV), 2021 (oral presentation)
Project page: https://chenhsuanlin.bitbucket.io/bundle-adjusting-NeRF
Paper: https://chenhsuanlin.bitbucket.io/bundle-adjusting-NeRF/paper.pdf
arXiv preprint: https://arxiv.org/abs/2104.06405
We provide PyTorch code for all experiments: planar image alignment, NeRF/BARF on both synthetic (Blender) and real-world (LLFF) datasets, and a template for BARFing on your custom sequence.
Prerequisites
- Note: for Azure ML support for this repository, please consider checking out this branch by Stan Szymanowicz.
This code is developed with Python3 (python3
). PyTorch 1.9+ is required.
It is recommended use Anaconda to set up the environment. Install the dependencies and activate the environment barf-env
with
conda env create --file requirements.yaml python=3
conda activate barf-env
Initialize the external submodule dependencies with
git submodule update --init --recursive
Dataset
-
Synthetic data (Blender) and real-world data (LLFF)
Both the Blender synthetic data and LLFF real-world data can be found in the NeRF Google Drive. For convenience, you can download them with the following script: (under this repo)
# Blender gdown --id 18JxhpWD-4ZmuFKLzKlAw-w5PpzZxXOcG # download nerf_synthetic.zip unzip nerf_synthetic.zip rm -f nerf_synthetic.zip mv nerf_synthetic data/blender # LLFF gdown --id 16VnMcF1KJYxN9QId6TClMsZRahHNMW5g # download nerf_llff_data.zip unzip nerf_llff_data.zip rm -f nerf_llff_data.zip mv nerf_llff_data data/llff
The
data
directory should contain the subdirectoriesblender
andllff
. If you already have the datasets downloaded, you can alternatively soft-link them within thedata
directory. -
<span style="color:red">Test your own sequence!</span>
If you want to try BARF on your own sequence, we provide a template data file in
data/iphone.py
, which is an example to read from a sequence captured by an iPhone 12. You should modifyget_image()
to read each image sample and set the raw image sizes (self.raw_H
,self.raw_W
) and focal length (self.focal
) according to your camera specs.
You may ignore the camera poses as they are assumed unknown in this case, which we simply set to zero vectors.
Running the code
-
BARF models
To train and evaluate BARF:
# <GROUP> and <NAME> can be set to your likes, while <SCENE> is specific to datasets # Blender (<SCENE>={chair,drums,ficus,hotdog,lego,materials,mic,ship}) python3 train.py --group=<GROUP> --model=barf --yaml=barf_blender --name=<NAME> --data.scene=<SCENE> --barf_c2f=[0.1,0.5] python3 evaluate.py --group=<GROUP> --model=barf --yaml=barf_blender --name=<NAME> --data.scene=<SCENE> --data.val_sub= --resume # LLFF (<SCENE>={fern,flower,fortress,horns,leaves,orchids,room,trex}) python3 train.py --group=<GROUP> --model=barf --yaml=barf_llff --name=<NAME> --data.scene=<SCENE> --barf_c2f=[0.1,0.5] python3 evaluate.py --group=<GROUP> --model=barf --yaml=barf_llff --name=<NAME> --data.scene=<SCENE> --resume
All the results will be stored in the directory
output/<GROUP>/<NAME>
. You may want to organize your experiments by grouping different runs in the same group.To train baseline models:
- Full positional encoding: omit the
--barf_c2f
argument. - No positional encoding: add
--arch.posenc!
.
If you want to evaluate a checkpoint at a specific iteration number, use
--resume=<ITER_NUMBER>
instead of just--resume
. - Full positional encoding: omit the
-
Training the original NeRF
If you want to train the reference NeRF models (assuming known camera poses):
# Blender python3 train.py --group=<GROUP> --model=nerf --yaml=nerf_blender --name=<NAME> --data.scene=<SCENE> python3 evaluate.py --group=<GROUP> --model=nerf --yaml=nerf_blender --name=<NAME> --data.scene=<SCENE> --data.val_sub= --resume # LLFF python3 train.py --group=<GROUP> --model=nerf --yaml=nerf_llff --name=<NAME> --data.scene=<SCENE> python3 evaluate.py --group=<GROUP> --model=nerf --yaml=nerf_llff --name=<NAME> --data.scene=<SCENE> --resume
If you wish to replicate the results from the original NeRF paper, use
--yaml=nerf_blender_repr
or--yaml=nerf_llff_repr
instead for Blender or LLFF respectively. There are some differences, e.g. NDC will be used for the LLFF forward-facing dataset. (The reference NeRF models considered in the paper do not use NDC to parametrize the 3D points.) -
Planar image alignment experiment
If you want to try the planar image alignment experiment, run:
python3 train.py --group=<GROUP> --model=planar --yaml=planar --name=<NAME> --seed=3 --barf_c2f=[0,0.4]
This will fit a neural image representation to a single image (default to
data/cat.jpg
), which takes a couple of minutes to optimize on a modern GPU. The seed number is set to reproduce the pre-generated warp perturbations in the paper. For the baseline methods, modify the arguments similarly as in the NeRF case above:- Full positional encoding: omit the
--barf_c2f
argument. - No positional encoding: add
--arch.posenc!
.
A video
vis.mp4
will also be created to visualize the optimization process. - Full positional encoding: omit the
-
Visualizing the results
We have included code to visualize the training over TensorBoard and Visdom. The TensorBoard events include the following:
- SCALARS: the rendering losses and PSNR over the course of optimization. For BARF, the rotational/translational errors with respect to the given poses are also computed.
- IMAGES: visualization of the RGB images and the RGB/depth rendering.
We also provide visualization of 3D camera poses in Visdom. Run
visdom -port 9000
to start the Visdom server.
The Visdom host server is default tolocalhost
; this can be overridden with--visdom.server
(seeoptions/base.yaml
for details). If you want to disable Visdom visualization, add--visdom!
.The
extract_mesh.py
script provides a simple way to extract the underlying 3D geometry using marching cubes. Run as follows:python3 extract_mesh.py --group=<GROUP> --model=barf --yaml=barf_blender --name=<NAME> --data.scene=<SCENE> --data.val_sub= --resume
This works for both BARF and the original NeRF (by modifying the command line accordingly). This is currently supported only for the Blender dataset.
Codebase structure
The main engine and network architecture in model/barf.py
inherit those from model/nerf.py
.
This codebase is structured so that it is easy to understand the actual parts BARF is extending from NeRF.
It is also simple to build your exciting applications upon either BARF or NeRF -- just inherit them again!
This is the same for dataset files (e.g. data/blender.py
).
To understand the config and command lines, take the below command as an example:
python3 train.py --group=<GROUP> --model=barf --yaml=barf_blender --name=<NAME> --data.scene=<SCENE> --barf_c2f=[0.1,0.5]
This will run model/barf.py
as the main engine with options/barf_blender.yaml
as the main config file.
Note that barf
hierarchically inherits nerf
(which inherits base
), making the codebase customizable.
The complete configuration will be printed upon execution.
To override specific options, add --<key>=value
or --<key1>.<key2>=value
(and so on) to the command line. The configuration will be loaded as the variable opt
throughout the codebase.
Some tips on using and understanding the codebase:
- The computation graph for forward/backprop is stored in
var
throughout the codebase. - The losses are stored in
loss
. To add a new loss function, just implement it incompute_loss()
and add its weight toopt.loss_weight.<name>
. It will automatically be added to the overall loss and logged to Tensorboard. - If you are using a multi-GPU machine, you can add
--gpu=<gpu_number>
to specify which GPU to use. Multi-GPU training/evaluation is currently not supported. - To resume from a previous checkpoint, add
--resume=<ITER_NUMBER>
, or just--resume
to resume from the latest checkpoint. - (to be continued....)
If you find our code useful for your research, please cite
@inproceedings{lin2021barf,
title={BARF: Bundle-Adjusting Neural Radiance Fields},
author={Lin, Chen-Hsuan and Ma, Wei-Chiu and Torralba, Antonio and Lucey, Simon},
booktitle={IEEE International Conference on Computer Vision ({ICCV})},
year={2021}
}
Please contact me (chlin@cmu.edu) if you have any questions!