Digging Into Normal Incorporated Stereo Matching
Zihua Liu Songyan Zhang Zhicheng Wang Masatoshi Okutomi
Despite the remarkable progress facilitated by learning-based stereo-matching
algorithms, disparity estimation in low-texture, occluded, and bordered regions
still remains a bottleneck that limits the performance. To tackle these
challenges, geometric guidance like plane information is necessary as it
provides intuitive guidance about disparity consistency and affinity
similarity. In this paper, we propose a normal incorporated joint learning
framework consisting of two specific modules named non-local disparity
propagation(NDP) and affinity-aware residual learning(ARL). The estimated
normal map is first utilized for calculating a non-local affinity matrix and a
non-local offset to perform spatial propagation at the disparity level. To
enhance geometric consistency, especially in low-texture regions, the estimated
normal map is then leveraged to calculate a local affinity matrix, providing
the residual learning with information about where the correction should refer
and thus improving the residual learning efficiency. Extensive experiments on
several public datasets including Scene Flow, KITTI 2015, and Middlebury 2014
validate the effectiveness of our proposed method. By the time we finished this
work, our approach ranked 1st for stereo matching across foreground pixels on
the KITTI 2015 dataset and 3rd on the Scene Flow dataset among all the
published works.