Digging Into Normal Incorporated Stereo Matching

What is it about?

Despite the remarkable progress facilitated by learning-based stereo matching algorithms, disparity estimation in low-texture, occluded, and bordered regions still remain bottlenecks that limit 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 as well as 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 which provides the residual learning with information about where the correction should refer and thus improve 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.

Featured Image

Photo by Marcos Paulo Prado on Unsplash

Photo by Marcos Paulo Prado on Unsplash

Why is it important?

We propose a normal incorporated joint learning framework to make full use of normal estimation information to provide intuitive geometric guidance for disparity estimation. The NDP and ARL module we proposed efficiently improves the disparity estimation in both texture-less(repetitive) and occluded regions.

Perspectives