Awesome
MINE: Continuous-Depth MPI with Neural Radiance Fields
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PyTorch implementation for our ICCV 2021 paper.<br><br>
MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis
Jiaxin Li*<sup>1</sup>,
Zijian Feng*<sup>1</sup>,
Qi She<sup>1</sup>,
Henghui Ding<sup>1</sup>,
Changhu Wang<sup>1</sup>,
Gim Hee Lee<sup>2</sup> <br>
<sup>1</sup>ByteDance, <sup>2</sup>National University of Singapore
*denotes equal contribution
Our MINE takes a single image as input and densely reconstructs the frustum of the camera, through which we can easily render novel views of the given scene:
The overall architecture of our method:
<img src='resources/pipeline.png'/>Run training on the LLFF dataset:
Firstly, set up your conda environment:
conda env create -f environment.yml
conda activate MINE
Download the pre-downsampled version of the LLFF dataset from Google Drive, unzip it and put it in the root of the project, then start training by running the following command:
sh start_training.sh MASTER_ADDR="localhost" MASTER_PORT=1234 N_NODES=1 GPUS_PER_NODE=2 NODE_RANK=0 WORKSPACE=/run/user/3861/vs_tmp DATASET=llff VERSION=debug EXTRA_CONFIG='{"training.gpus": "0,1"}'
You may find the tensorboard logs and checkpoints in the sub-working directory (WORKSPACE + VERSION).
Apart from the LLFF dataset, we experimented on the RealEstate10K, KITTI Raw and the Flowers Light Fields datasets - the data pre-processing codes and training flow for these datasets will be released later.
Running our pretrained models:
We release the pretrained models trained on the RealEstate10K, KITTI and the Flowers datasets:
Dataset | N | Input Resolution | Download Link |
---|---|---|---|
RealEstate10K | 32 | 384x256 | Google Drive |
RealEstate10K | 64 | 384x256 | Google Drive |
KITTI | 32 | 768x256 | Google Drive |
KITTI | 64 | 768x256 | Google Drive |
Flowers | 32 | 512x384 | Google Drive |
Flowers | 64 | 512x384 | Google Drive |
To run the models, download the checkpoint and the hyper-parameter yaml file and place them in the same directory, then run the following script:
python3 visualizations/image_to_video.py --checkpoint_path MINE_realestate10k_384x256_monodepth2_N64/checkpoint.pth --gpus 0 --data_path visualizations/home.jpg --output_dir .
Citation
If you find our work helpful to your research, please cite our paper:
@inproceedings{mine2021,
title={MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis},
author={Jiaxin Li and Zijian Feng and Qi She and Henghui Ding and Changhu Wang and Gim Hee Lee},
year={2021},
booktitle={ICCV},
}