https://github.com/mjiUST/SurfaceNet-plus

2020 TPAMI, SurfaceNet+ is a volumetric learning framework for the very sparse MVS. The sparse-MVS benchmark is maintained here. Authors: Mengqi Ji#, Jinzhi Zhang#, Qionghai Dai, Lu Fang.
https://github.com/mjiUST/SurfaceNet-plus

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2020 TPAMI, SurfaceNet+ is a volumetric learning framework for the very sparse MVS. The sparse-MVS benchmark is maintained here. Authors: Mengqi Ji#, Jinzhi Zhang#, Qionghai Dai, Lu Fang.

• Host: GitHub
• URL: https://github.com/mjiUST/SurfaceNet-plus
• Owner: mjiUST
• Created: 2020-05-22T02:10:37.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-07-25T09:06:32.000Z (over 4 years ago)
• Last Synced: 2024-08-01T20:36:56.332Z (3 months ago)
• Homepage:
• Size: 9.1 MB
• Stars: 78
• Watchers: 14
• Forks: 8
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-computer-vision-papers - 2020PAMI

README

        
# SurfaceNet+

- An End-to-end 3D Neural Network for Very Sparse MVS. 

    * 2020TPAMI [early access link](https://ieeexplore.ieee.org/document/9099504).

    * or Arxiv [preprint version](https://www.researchgate.net/publication/341647549_SurfaceNet_An_End-to-end_3D_Neural_Network_for_Very_Sparse_Multi-view_Stereopsis/figures).

- **Key contributions**

    1. Proposed a Sparse-MVS benchmark (under construction)

        * Comprehensive evaluation on the datasets: [DTU](http://roboimagedata.compute.dtu.dk/?page_id=36), [Tanks and Temples](https://www.tanksandtemples.org/), etc.

    2. Proposed a **trainable occlusion-aware** view selection scheme for the volumetric MVS method, e.g., [SurfaceNet](https://github.com/mjiUST/SurfaceNet)[5]. 

    3. Analysed the advantages of the volumetric methods, e.g., [SurfaceNet](https://github.com/mjiUST/SurfaceNet)[5] and SurfaceNet+, on the **Sparse-MVS problem** over the depth-fusion methods, e.g., [Gipuma](https://github.com/kysucix/gipuma) [6], [R-MVSNet](https://github.com/YoYo000/MVSNet)[7], [Point-MVSNet](https://github.com/callmeray/PointMVSNet)[8], and [COLMAP](https://github.com/colmap/colmap)[9].

# [Sparse-MVS Benchmark](http://sparse-mvs.com) 



  



## (1) [Sparse-MVS of the DTU dataset](http://sparse-mvs.com/leaderboard.html)



  

  

  **Fig.1**: Illustration of a very sparse MVS setting using only $1/7$ of the camera views, i.e., $\{v_i\}_{i=1,8,15,22,...}$, to recover the model 23 in the DTU dataset [10]. Compared with the state-of-the-art methods, the proposed SurfaceNet+ provides much complete reconstruction, especially around the boarder region captured by very sparse views.





  

  

  **Fig.2**: Comparison with the existing methods in the DTU Dataset [10] with different sparsely sampling strategy. When Sparsity = 3 and Batchsize = 2, the chosen camera indexes are 1,2 / 4,5 / 7,8 / 10,11 / .... SurfaceNet+ constantly outperforms the state-of-the-art methods at all the settings, especially at the very sparse scenario.



## (2) [Sparse-MVS of the T&T dataset](http://sparse-mvs.com/leaderboard.html)



  

  

  **Fig.3**: Results of a tank model in the Tanks and Temples 'intermediate' set [23] compared with R-MVSNet [7] and COLMAP [9], which demonstrate the power of SurfaceNet+ of high recall prediction in the sparse-MVS setting.



# Citing

If you find SurfaceNet+, the Sparse-MVS benchmark, or [SurfaceNet](https://github.com/mjiUST/SurfaceNet) useful in your research, please consider citing:

    @article{ji2020surfacenet_plus,

        title={SurfaceNet+: An End-to-end 3D Neural Network for Very Sparse Multi-view Stereopsis},

        author={Ji, Mengqi and Zhang, Jinzhi and Dai, Qionghai and Fang, Lu},

        journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},

        year={2020},

        publisher={IEEE}

   }

    @inproceedings{ji2017surfacenet,

        title={SurfaceNet: An End-To-End 3D Neural Network for Multiview Stereopsis},

        author={Ji, Mengqi and Gall, Juergen and Zheng, Haitian and Liu, Yebin and Fang, Lu},

        booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},

        pages={2307--2315},

        year={2017}

    }