Planar Prior Assisted PatchMatch Multi-View Stereo


  • Qingshan Xu Huazhong University of Science and Technology
  • Wenbing Tao Huazhong University of Science and Technology



The completeness of 3D models is still a challenging problem in multi-view stereo (MVS) due to the unreliable photometric consistency in low-textured areas. Since low-textured areas usually exhibit strong planarity, planar models are advantageous to the depth estimation of low-textured areas. On the other hand, PatchMatch multi-view stereo is very efficient for its sampling and propagation scheme. By taking advantage of planar models and PatchMatch multi-view stereo, we propose a planar prior assisted PatchMatch multi-view stereo framework in this paper. In detail, we utilize a probabilistic graphical model to embed planar models into PatchMatch multi-view stereo and contribute a novel multi-view aggregated matching cost. This novel cost takes both photometric consistency and planar compatibility into consideration, making it suited for the depth estimation of both non-planar and planar regions. Experimental results demonstrate that our method can efficiently recover the depth information of extremely low-textured areas, thus obtaining high complete 3D models and achieving state-of-the-art performance.




How to Cite

Xu, Q., & Tao, W. (2020). Planar Prior Assisted PatchMatch Multi-View Stereo. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12516-12523.



AAAI Technical Track: Vision