# BlendedMVS
**Repository Path**: ahangchen/BlendedMVS
## Basic Information
- **Project Name**: BlendedMVS
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-02-06
- **Last Updated**: 2024-11-24
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# BlendedMVS
## About
[BlendedMVS](https://arxiv.org/abs/1911.10127) is a large-scale MVS dataset for generalized multi-view stereo networks. The dataset contains 17k MVS training samples covering a variety of 113 scenes, including architectures, sculptures and small objects.
## Upgrade to BlendedMVG
BlendedMVG, a superset of [BlendedMVS](https://arxiv.org/abs/1911.10127), is a multi-purpose large-scale dataset for solving multi-view geometry related problems. Except for the 113 scenes in BlendedMVS dataset, we follow its blending procedure to generate 389 more scenes (originally shown in [GL3D](https://github.com/lzx551402/GL3D)) for BlendedMVG. The training image number is increased from 17k to over 110k.
BlendedMVG and its preceding works ([BlendedMVS](https://arxiv.org/abs/1911.10127) and [GL3D](https://github.com/lzx551402/GL3D)) have been applied to several key 3D computer vision tasks, including image retrieval, image feature detection and description, two-view outlier rejection and multi-view stereo. If you find BlendedMVS or BlendedMVG useful for your research, please cite:
```
@article{yao2020blendedmvs,
title={BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks},
author={Yao, Yao and Luo, Zixin and Li, Shiwei and Zhang, Jingyang and Ren, Yufan and Zhou, Lei and Fang, Tian and Quan, Long},
journal={Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
```
## License
BlendedMVS and BlendedMVG are licensed under a Creative Commons Attribution 4.0 International License!!!
## Download
For MVS networks, BlendedMVG is preprocessed and split into 3 smaller subsets (BlendedMVS, BlendedMVS+ and BlendedMVS++):
|Dataset | Resolution (768 x 576) | Resolution (2048 x 1536) | Supplementaries |
|:--------------:|:---------------:|:----------------------------------:|:---------------:|
|BlendedMVS | [low-res set](https://1drv.ms/u/s!Ag8Dbz2Aqc81gVDu7FHfbPZwqhIy?e=BHY07t) (27.5 GB) | [high-res set](https://1drv.ms/u/s!Ag8Dbz2Aqc81ezb9OciQ4zKwJ_w?e=afFOTi) (156 GB) | [textured meshes](https://1drv.ms/u/s!Ag8Dbz2Aqc81fkvi2X9Mmzan0FI?e=7x2WoS) (9.42 GB), [other images](https://1drv.ms/u/s!Ag8Dbz2Aqc81gVMgQoHpAJP4jlwo?e=wVOWqD) (7.56 GB) |
|BlendedMVS+|[low-res set](https://1drv.ms/u/s!Ag8Dbz2Aqc81gVLILxpohZLEYiIa?e=MhwYSR) (81.5 GB) | - | - |
|BlendedMVS++|[low-res set](https://1drv.ms/u/s!Ag8Dbz2Aqc81gVHCxmURGz0UBGns?e=Tnw2KY) (80.0 GB) | - | - |
Experiments in [BlendedMVS paper](https://arxiv.org/abs/1911.10127) were conducting using the BlendedMVS low-res-dataset. In most cases, the low-res dataset would be enough.
## Dataset Structure
BlendedMVS(G) dataset adopts MVSNet input format. Please structure your dataset as listed below after downloading the whole dataset:
```
DATA_ROOT
├── BlendedMVG_list.txt
├── BlendedMVS_list.txt
├── BlendedMVS+_list.txt
├── BlendedMVS++_list.txt
├── ...
├── PID0
│ ├── blended_images
│ │ ├── 00000000.jpg
│ │ ├── 00000000_masked.jpg
│ │ ├── 00000001.jpg
│ │ ├── 00000001_masked.jpg
│ │ └── ...
│ ├── cams
│ │ ├── pair.txt
│ │ ├── 00000000_cam.txt
│ │ ├── 00000001_cam.txt
│ │ └── ...
│ └── rendered_depth_maps
│ ├── 00000000.pfm
│ ├── 00000001.pfm
│ └── ...
├── PID1
├── ...
└── PID501
```
``PID`` here is the unique project ID listed in the ``BlendedMVG_list.txt`` file. We provide blended images with and without masks. For detailed file formats, please refer to [MVSNet](https://github.com/YoYo000/MVSNet).
## What you can do with BlendedMVS(G)?
Please refer to following repositories on how to apply BlendedMVS(G) on multi-view stereo and feature detector/descriptor networks:
|Tasks |Repositories |
|:--------------:|:--------------------------------------------------:|
|Multi-view stereo | [MVSNet & R-MVSNet](https://github.com/YoYo000/MVSNet) |
|Descriptors & Detectors| [GL3D](https://github.com/lzx551402/GL3D) & [ASLFeat](https://github.com/lzx551402/ASLFeat) & [ContextDesc](https://github.com/lzx551402/contextdesc) & [GeoDesc](https://github.com/lzx551402/geodesc) |
Except for the above tasks, we believe BlendedMVS(G) could also be applied to a variety of geometry related problems, including, but not limited to:
* Sparse outlier rejection ([OANet](https://github.com/zjhthu/OANet), tested with the original GL3D)
* Image retrieval ([MIRorR](https://github.com/hlzz/mirror), tested with the original GL3D)
* Single-view depth/normal estimation
* Two-view disparity estimation
* Single/multi-view camera pose regression
Feel free to modify the dataset and adjust to your own tasks!
## Note
* Online augmentation should be implemented by users themselves. An example for tensorflow users could be found in [MVSNet](https://github.com/YoYo000/MVSNet). An example for pytorch users could be found in [CasMVSNet_pl](https://github.com/kwea123/CasMVSNet_pl)
* The number of selected source images for a given reference image might be smaller than 10 (when parsing pair.txt).
* The `depth_min` and `depth_max` in ground truth cameras might be smaller or equal to zero (very few, when parsing *_cam.txt).
* The rendered depth map and blended images might be empty as the textured mesh model is not necessarily to be complete (when dealing with *.pfm and *.jpg files).
## Changelog
### 2020 April 13:
* Upgrade to BlendedMVG dataset!
### 2020 April 13:
* Upload BlendedMVS textured mesh models
* Upload BlendedMVS high-res dataset
* Upload input and rendered images (low-res)
* Fix bug on multi-texture mesh rendering, update BlendedMVS low-res dataset.
### 2022 June 8:
* Fix download links