MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching
Miaojie Feng, Junda Cheng, Hao Jia, Longliang Liu, Gangwei Xu,, Qingyong Hu, Xin Yang

TL;DR
MC-Stereo introduces a novel iterative stereo matching framework that effectively handles multi-peak distributions and employs a cascade search range, achieving state-of-the-art results on major benchmarks.
Contribution
The paper proposes MC-Stereo, a new iterative optimization architecture with multi-peak lookup and cascade search range, plus a pre-trained feature extractor, advancing stereo matching accuracy.
Findings
Ranks first on KITTI-2012 and KITTI-2015 benchmarks.
Achieves state-of-the-art performance on ETH3D.
Effectively handles multi-peak distributions in stereo matching.
Abstract
Stereo matching is a fundamental task in scene comprehension. In recent years, the method based on iterative optimization has shown promise in stereo matching. However, the current iteration framework employs a single-peak lookup, which struggles to handle the multi-peak problem effectively. Additionally, the fixed search range used during the iteration process limits the final convergence effects. To address these issues, we present a novel iterative optimization architecture called MC-Stereo. This architecture mitigates the multi-peak distribution problem in matching through the multi-peak lookup strategy, and integrates the coarse-to-fine concept into the iterative framework via the cascade search range. Furthermore, given that feature representation learning is crucial for successful learn-based stereo matching, we introduce a pre-trained network to serve as the feature extractor,…
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Taxonomy
TopicsAdvanced Vision and Imaging · Satellite Image Processing and Photogrammetry · Infrared Target Detection Methodologies
