A novel stereo matching pipeline with robustness and unfixed disparity search range
This work addresses limitations in stereo matching for applications like 3D multimedia and VR/AR, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of stereo matching's poor generalization and fixed disparity search range by proposing a new pipeline that computes semi-dense disparity maps with binocular disparity and completes them with monocular cues, resulting in better generalization, relaxed search range limitations, and the ability to handle both positive and negative disparities.
Stereo matching is an essential basis for various applications, but most stereo matching methods have poor generalization performance and require a fixed disparity search range. Moreover, current stereo matching methods focus on the scenes that only have positive disparities, but ignore the scenes that contain both positive and negative disparities, such as 3D movies. In this paper, we present a new stereo matching pipeline that first computes semi-dense disparity maps based on binocular disparity, and then completes the rest depending on monocular cues. The new stereo matching pipeline have the following advantages: It 1) has better generalization performance than most of the current stereo matching methods; 2) relaxes the limitation of a fixed disparity search range; 3) can handle the scenes that involve both positive and negative disparities, which has more potential applications, such as view synthesis in 3D multimedia and VR/AR. Experimental results demonstrate the effectiveness of our new stereo matching pipeline.