关键词: 有监督立体匹配, 卷积神经网络, 迁移学习, 跨域, 视差分数

Abstract:

Convolutional Neural Networks （CNNs） have made good progress in supervised stereo matching tasks， but most CNN algorithms are difficult to perform well in cross-domain situations. Aiming at the stereo matching problem of cross-domain data， a Cross-domain Adaptation Stereo Matching Network （CASM-Net） algorithm was proposed to achieve domain adaptive stereo matching tasks using transfer learning based on CNN. In the algorithm， a transferable feature extraction module was used to extract rich wide-domain features for stereo matching tasks. At the same time， an adaptive cost optimization module was designed to obtain the optimal cost distribution by making use of the similarity information on different receptive fields to optimize the cost. In addition， a disparity score prediction module was proposed to quantify the stereo matching ability of different regions， and the disparity results were further optimized by adjusting the disparity search range of the image. Experimental results show that on KITTI2012 and KITTI2015 datasets， compared with PSMNet （Pyramid Stereo Matching Network） algorithm， CASM-Net algorithm reduces 6.1%， 3.3% and 19.3% in 2-PE-Noc， 2-PE-All and 3-PE-fg， respectively； on Middlebury dataset， without re-training， CASM-Net algorithm achieves the optimal or suboptimal 2-PE results on all samples in the comparison with other algorithms. It can be seen that CASM-Net algorithm can improve cross-domain stereo matching.

Key words: supervised stereo matching, Convolutional Neural Network (CNN), transfer learning, cross-domain, disparity score