2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDIMT-1.5
Paper Title MPANet: Multi-scale Pyramid Aggregation Network for Stereo Matching
Authors Ziyu Zhu, Wei Guo, Wei Chen, Qiuping Li, Yong Zhao, Shenzhen Graduate School of Peking University, China
SessionIMT-1: Computational Imaging Learning-based Models
LocationArea J
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Computational Imaging Methods and Models: Learning-Based Models
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract At present, the performance of the end-to-end stereo matching networks based on CNN greatly exceed the traditional stereo matching networks, but the accuracy in those ill-posed regions like foreground areas is still not optimistic. In this paper, we propose a novel design to improve the prediction performance of disparity in foreground. First, a multi-scale pyramid aggregation module with hourglass-like structure is designed to effectively utilize the aggregation information of different scales. In addition, we propose MPANet, a novel end-to-end stereo matching network, which significantly alleviates the disparity mismatch problem in foreground areas. Experimental results on Scene Flow and KITTI datasets also demonstrate that our network has competitive performance among existing first-class methods.