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https://apchenstu.github.io/mvsnerf/

[ICCV 2021] Our work presents a novel neural rendering approach that can efficiently reconstruct geometric and neural radiance fields for view synthesis.
https://apchenstu.github.io/mvsnerf/

3d-reconstruction nerf

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[ICCV 2021] Our work presents a novel neural rendering approach that can efficiently reconstruct geometric and neural radiance fields for view synthesis.

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README 

        
# MVSNeRF

## [Project page](https://apchenstu.github.io/mvsnerf/) |  [Paper](https://arxiv.org/abs/2103.15595)

This repository contains a pytorch lightning implementation for the ICCV 2021 paper: [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://arxiv.org/abs/2103.15595). Our work present a novel neural rendering approach that can efficiently reconstruct

geometric and neural radiance fields for view synthesis, Moreover, if dense images are captured, our estimated radiance field representation can be easily fine-tuned; this leads to fast per-scene reconstruction.




![Pipeline](configs/pipeline.png)



## Installation



#### Tested on Ubuntu 20.04 + Pytorch 1.10.1 + Pytorch Lignting 1.3.5



Install environment:

```

conda create -n mvsnerf python=3.8

conda activate mvsnerf

pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

pip install pytorch-lightning==1.3.5 imageio pillow scikit-image opencv-python configargparse lpips kornia warmup_scheduler matplotlib test-tube imageio-ffmpeg

```



## Training

Please see each subsection for training on different datasets. Available training datasets:



* [DTU](#dtu)

* [Blender](#blender) (Realistic Synthetic)

* [LLFF](#llff) (Real Forward-Facing)

* [Your own data](#your-own-data) (images/intrinsic/extrinsic/nearfar boundles)



### DTU dataset



#### Data download



Download the preprocessed [DTU training data](https://drive.google.com/file/d/1eDjh-_bxKKnEuz5h-HXS7EDJn59clx6V/view)

and [Depth_raw](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/cascade-stereo/CasMVSNet/dtu_data/dtu_train_hr/Depths_raw.zip) from original [MVSNet repo](https://github.com/YoYo000/MVSNet)

and unzip. We provide a [DTU example](https://1drv.ms/u/s!AjyDwSVHuwr8zhAAXh7x5We9czKj?e=oStQ48), please

follow with the example's folder structure.



#### Training model



Run

```

CUDA_VISIBLE_DEVICES=$cuda  python train_mvs_nerf_pl.py \

   --expname $exp_name

   --num_epochs 6

   --use_viewdirs \

   --dataset_name dtu \

   --datadir $DTU_DIR

```

More options refer to the `opt.py`, training command example:

```

CUDA_VISIBLE_DEVICES=0  python train_mvs_nerf_pl.py

    --with_depth  --imgScale_test 1.0 \

    --expname mvs-nerf-is-all-your-need \

    --num_epochs 6 --N_samples 128 --use_viewdirs --batch_size 1024 \

    --dataset_name dtu \

    --datadir path/to/dtu/data \

    --N_vis 6

```



You may need to add `--with_depth` if you want to quantity depth during training. `--N_vis` denotes the validation frequency.

`--imgScale_test` is the downsample ratio during validation, like 0.5. The training process takes about 30h on single RTX 2080Ti

for 6 epochs. 



*Important*: please always set batch_size to 1 when you are trining a genelize model, you can enlarge it when fine-tuning.



*Checkpoint*: a pre-trained checkpint is included in `ckpts/mvsnerf-v0.tar`. 



*Evaluation*: We also provide a rendering and quantity scipt  in `renderer.ipynb`, 

and you can also use the run_batch.py if you want to testing or finetuning on different dataset. More results can be found from

[Here](https://drive.google.com/drive/folders/1ko8OW38iDtj4fHvX0e3Wom9YvtJNTSXu?usp=sharing),

please check your configuration if your rendering result looks absnormal.



Rendering from the trained model should have result like this:



![no-finetuned](https://user-images.githubusercontent.com/16453770/124207949-210b8300-db19-11eb-9ab9-610eff35395e.gif)



## Finetuning

### Blender



  Steps



#### Data download



Download `nerf_synthetic.zip` from [here](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1)



```

CUDA_VISIBLE_DEVICES=0  python train_mvs_nerf_finetuning_pl.py  \

    --dataset_name blender --datadir /path/to/nerf_synthetic/lego \

    --expname lego-ft  --with_rgb_loss  --batch_size 1024  \

    --num_epochs 1 --imgScale_test 1.0 --white_bkgd  --pad 0 \

    --ckpt ./ckpts/mvsnerf-v0.tar --N_vis 1

```



### LLFF



  Steps



#### Data download



Download `nerf_llff_data.zip` from [here](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1)



```

CUDA_VISIBLE_DEVICES=0  python train_mvs_nerf_finetuning_pl.py  \

    --dataset_name llff --datadir /path/to/nerf_llff_data/{scene_name} \

    --expname horns-ft  --with_rgb_loss  --batch_size 1024  \

    --num_epochs 1 --imgScale_test 1.0  --pad 24 \

    --ckpt ./ckpts/mvsnerf-v0.tar --N_vis 1

```



### DTU



  Steps



```

CUDA_VISIBLE_DEVICES=0  python train_mvs_nerf_finetuning_pl.py  \

    --dataset_name dtu_ft --datadir /path/to/DTU/mvs_training/dtu/scan1 \

    --expname scan1-ft  --with_rgb_loss  --batch_size 1024  \

    --num_epochs 1 --imgScale_test 1.0   --pad 24 \

    --ckpt ./ckpts/mvsnerf-v0.tar --N_vis 1

```



## Rendering

After training or finetuning, you can render free-viewpoint videos

with the `renderer-video.ipynb`. if you want to use your own data,

please using the right hand coordinate system (intrinsic, nearfar and extrinsic either with

camera to world or world to camera in opencv format) and modify the rendering scipts.



After 10k iterations (~ 15min), you should have videos like this:



![finetuned](https://user-images.githubusercontent.com/16453770/124207013-15b75800-db17-11eb-8d96-e8dbe4181c98.gif)



## Citation

If you find our code or paper helps, please consider citing:

```

@article{chen2021mvsnerf,

  title={MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo},

  author={Chen, Anpei and Xu, Zexiang and Zhao, Fuqiang and Zhang, Xiaoshuai and Xiang, Fanbo and Yu, Jingyi and Su, Hao},

  journal={arXiv preprint arXiv:2103.15595},

  year={2021}

}

```



Big thanks to [**CasMVSNet_pl**](https://github.com/kwea123/CasMVSNet_pl), our code is partially

borrowing from them.



## Relevant Works

[**MVSNet: Depth Inference for Unstructured Multi-view Stereo (ECCV 2018)**](https://arxiv.org/abs/1804.02505)


Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, Long Quan



[**Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching (CVPR 2020)**](https://arxiv.org/abs/1912.06378)


Xiaodong Gu, Zhiwen Fan, Zuozhuo Dai, Siyu Zhu, Feitong Tan, Ping Tan



[**NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (ECCV 2020)**](http://www.matthewtancik.com/nerf)


Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng



[**IBRNet: Learning Multi-View Image-Based Rendering (CVPR 2021)**](https://ibrnet.github.io/)


Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser



[**PixelNeRF: Neural Radiance Fields from One or Few Images (CVPR 2021)**](https://alexyu.net/pixelnerf/)


Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa