Depth-supervised NeRF: Fewer Views and Faster Training for Free

Project | Paper | YouTube

Pytorch implementation of DS-NeRF. DS-NeRF can improve the training of neural radiance fields by leveraging depth supervision derived from 3D point clouds. It can be used to train NeRF models given only very few input views.

Depth-supervised NeRF: Fewer Views and Faster Training for Free

CVPR, 2022

Kangle Deng¹, Andrew Liu², Jun-Yan Zhu¹, Deva Ramanan^1,3,

¹CMU, ²Google, ³Argo AI

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We propose DS-NeRF (Depth-supervised Neural Radiance Fields), a model for learning neural radiance fields that takes advantage of depth supervised by 3D point clouds. Current NeRF methods require many images with known camera parameters -- typically produced by running structure-from-motion (SFM) to estimate poses and a sparse 3D point cloud. Most, if not all, NeRF pipelines make use of the former but ignore the latter. Our key insight is that such sparse 3D input can be used as an additional free signal during training.

Results

NeRF trained with 2 views:

DS-NeRF trained with 2 views:

NeRF trained with 5 views:

DS-NeRF trained with 5 views:

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Quick Start

Dependencies

Install requirements:

pip install -r requirements.txt

You will also need COLMAP installed to compute poses if you want to run on your data.

Data

Download the LLFF dataset here.

To play with other scenes presented in the paper, download the data here.

Pre-trained Models

You can download our pre-trained models using the following script:

bash download_models.sh

See the following directory structure for an example:

├── logs 
│   ├── fern_2v    # downloaded logs
│   ├── flower_2v  # downloaded logs

How to Run?

Generate camera poses and sparse depth information using COLMAP (optional)

This step is necessary only when you want to run on your data.

First, place your scene directory somewhere. See the following directory structure for an example:

├── data
│   ├── fern_2v
│   ├── ├── images
│   ├── ├── ├── image001.png
│   ├── ├── ├── image002.png

To generate the poses and sparse point cloud:

python imgs2poses.py <your_scenedir>

Note: if you use this data format, make sure your dataset_type in the config file is set as llff.

Testing

Once you have the experiment directory (downloaded or trained on your own) in ./logs,

• to render a video:

python run_nerf.py --config configs/fern_dsnerf.txt --render_only

The video would be stored in the experiment directory.

Training

To train a DS-NeRF on the example fern dataset:

python run_nerf.py --config configs/fern_dsnerf.txt

It will create an experiment directory in ./logs, and store the checkpoints and rendering examples there.

You can create your own experiment configuration to try other datasets.

Use depth-supervised loss in your own project

We provide a tutorial on how to use depth-supervised loss in your own project here.

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Citation

If you find this repository useful for your research, please cite the following work.

@InProceedings{kangle2021dsnerf,
    author    = {Deng, Kangle and Liu, Andrew and Zhu, Jun-Yan and Ramanan, Deva},
    title     = {Depth-supervised {NeRF}: Fewer Views and Faster Training for Free},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022}
}

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Acknowledgments

This code borrows heavily from nerf-pytorch. We thank Takuya Narihira, Akio Hayakawa, Sheng-Yu Wang, and for helpful discussion. We are grateful for the support from Sony Corporation, Singapore DSTA, and the CMU Argo AI Center for Autonomous Vehicle Research.