MVSNet & R-MVSNet

About

MVSNet is a deep learning architecture for depth map inference from unstructured multi-view images, and R-MVSNet is its extension for scalable learning-based MVS reconstruction. If you find this project useful for your research, please cite:

@article{yao2018mvsnet,
  title={MVSNet: Depth Inference for Unstructured Multi-view Stereo},
  author={Yao, Yao and Luo, Zixin and Li, Shiwei and Fang, Tian and Quan, Long},
  journal={European Conference on Computer Vision (ECCV)},
  year={2018}
}

@article{yao2019recurrent,
  title={Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference},
  author={Yao, Yao and Luo, Zixin and Li, Shiwei and Shen, Tianwei and Fang, Tian and Quan, Long},
  journal={Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}

How to Use

Installation

Check out the source code git clone https://github.com/YoYo000/MVSNet
Install cuda 9.0, cudnn 7.0 and python 2.7
Install Tensorflow and other dependencies by sudo pip install -r requirements.txt

Training

Download the preprocessed DTU training data (also available at Baiduyun, code: s2v2), and upzip it as the MVS_TRANING folder
Enter the MVSNet/mvsnet folder, in train.py, set dtu_data_root to your MVS_TRANING path
Create a log folder and a model folder in wherever you like to save the training outputs. Set the log_dir and save_dir in train.py correspondingly
Train MVSNet (GTX1080Ti): python train.py --regularization '3DCNNs'
Train R-MVSNet (GTX1080Ti): python train.py --regularization 'GRU'

Testing

Download the test data for scan9 and unzip it as the TEST_DATA_FOLDER folder, which should contain one cams folder, one images folder and one pair.txt file
Download the pre-trained MVSNet and R-MVSNet models and upzip the file as MODEL_FOLDER
Enter the MVSNet/mvsnet folder, in test.py, set model_dir to MODEL_FOLDER
Run MVSNet (GTX1080Ti): python test.py --dense_folder TEST_DATA_FOLDER --regularization '3DCNNs' --max_w 1152 --max_h 864 --max_d 192 --interval_scale 1.06
Run R-MVSNet (GTX1080Ti): python test.py --dense_folder TEST_DATA_FOLDER --regularization 'GRU' --max_w 1600 --max_h 1200 --max_d 256 --interval_scale 0.8
Inspect the .pfm format outputs in TEST_DATA_FOLDER/depths_mvsnet using python visualize.py .pfm. For example the depth map and probability map for image 00000012 should be something like:


reference image	depth map	probability map

Post-Processing

R/MVSNet itself only produces per-view depth maps. To generate the 3D point cloud, we need to apply depth map filter/fusion for post-processing. As our implementation of this part is depended on the Altizure internal library, currently we could not provide the corresponding code. Fortunately, depth map filter/fusion is a general step in MVS reconstruction, and there are similar implementations in other open-source MVS algorithms. We provide the script depthfusion.py to utilize fusibile for post-processing (thank Silvano Galliani for the excellent code!).

To run the post-processing:

Check out the modified version fusibile git clone https://github.com/YoYo000/fusibile
Install fusibile by cmake . and make, which will generate the executable at FUSIBILE_EXE_PATH
Run post-processing (--prob_threshold 0.8 if using 3DCNNs): python depthfusion.py --dense_folder TEST_DATA_FOLDER --fusibile_exe_path FUSIBILE_EXE_PATH --prob_threshold 0.3
The final point cloud is stored in TEST_DATA_FOLDER/points_mvsnet/consistencyCheck-TIME/final3d_model.ply.

We observe that the point cloud output of depthfusion.py is very similar to our own implementation. For detailed differences, please refer to MVSNet paper and Galliani's paper. The point cloud for scan9 should look like:


point cloud result	ground truth point cloud

Reproduce Benchmarking Results

The following steps are required to reproduce the point cloud results:

Generate R/MVSNet inputs from the SfM outputs, you can use our preprocessed inputs for DTU, Tanks and Temples and ETH3D datasets (provided)
Run R/MVSNet test script to generate depth maps for all views (provided)
Apply variational depth map refinement for all views (optional, not provided)
Apply depth map filter and fusion to generate the point cloud results (partially provided via fusibile)

R-MVSNet point cloud results with full post-processing are also provided: DTU evaluation point clouds

File Formats

Each project folder should contain the following

.                          
├── images                 
│   ├── 00000000.jpg       
│   ├── 00000001.jpg       
│   └── ...                
├── cams                   
│   ├── 00000000_cam.txt   
│   ├── 00000001_cam.txt   
│   └── ...                
└── pair.txt

If you want to apply R/MVSNet to your own data, please structure your data into such a folder. We also provide a script colmap2mvsnet.py to convert COLMAP SfM result to R/MVSNet input.

Image Files

All image files are stored in the images folder. We index each image using an 8 digit number starting from 00000000. The following camera and output files use the same indexes as well.

Camera Files

The camera parameter of one image is stored in a cam.txt file. The text file contains the camera extrinsic E = [R|t], intrinsic K and the depth range:

extrinsic
E00 E01 E02 E03
E10 E11 E12 E13
E20 E21 E22 E23
E30 E31 E32 E33

intrinsic
K00 K01 K02
K10 K11 K12
K20 K21 K22

DEPTH_MIN DEPTH_INTERVAL (DEPTH_NUM DEPTH_MAX)

Note that the depth range and depth resolution are determined by the minimum depth DEPTH_MIN, the interval between two depth samples DEPTH_INTERVAL, and also the depth sample number DEPTH_NUM (or max_d in the training/testing scripts if DEPTH_NUM is not provided). We also left the interval_scale for controlling the depth resolution. The maximum depth is then computed as:

DEPTH_MAX = DEPTH_MIN + (interval_scale * DEPTH_INTERVAL) * (max_d - 1)

View Selection File

We store the view selection result in the pair.txt. For each reference image, we calculate its view selection scores with each of the other views, and store the 10 best views in the pair.txt file:

TOTAL_IMAGE_NUM
IMAGE_ID0                       # index of reference image 0 
10 ID0 SCORE0 ID1 SCORE1 ...    # 10 best source images for reference image 0 
IMAGE_ID1                       # index of reference image 1
10 ID0 SCORE0 ID1 SCORE1 ...    # 10 best source images for reference image 1 
...

MVSNet input from COLMAP SfM

We provide a script to convert COLMAP SfM result to R/MVSNet input. After recovering SfM result and undistorting all images, COLMAP should generate a dense folder COLMAP/dense/ containing an undistorted image folder COLMAP/dense/images/ and a undistorted camera folder COLMAP/dense/sparse/. Then, you can use the following command to generate the R/MVSNet input:

python colmap2mvsnet.py --dense_folder COLMAP/dense

The depth sample number will be automatically computed using the inverse depth setting. If you want to generate the MVSNet input with a fixed depth sample number (e.g., 256), you could specified the depth number via --max_d 256.

Output Format

The test.py script will create a depths_mvsnet folder to store the running results, including the depth maps, probability maps, scaled/cropped images and the corresponding cameras. The depth and probability maps are stored in .pfm format. We provide the python IO for pfm files in the preprocess.py script, and for the c++ IO, we refer users to the Cimg library. To inspect the pfm format results, you can simply type python visualize.py .pfm.

Todo

Validation script

Changelog

2019 Feb 28

Use tf.contrib.image.transform for differentiable homography warping. Reconstruction is now x2 faster!

2019 March 1

Implement R-MVSNet and GRU regularization
Network change: enable scale and center in batch normalization
Network change: replace UniNet with 2D UNet
Network change: use group normalization in R-MVSNet

2019 March 7

MVSNet / R-MVSNet and training / testing scripts
MVSNet and R-MVSNet models (trained for 100000 iterations)

2019 March 11

Add "Reproduce Benchmarking Results" section

2019 March 14

Add R-MVSNet point clouds of DTU evaluation set

2019 April 10

Add Baiduyun (code: s2v2) link for mainland China users

2019 April 29

Add colmap2mvsnet.py script to convert COLMAP SfM result to MVSNet input, including depth range estimation and view selection

SSground/MVSNet