  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,
enhancing the front end of the stereo matching pipeline. Based on these
improvements, MC-Stereo ranks first among all publicly available methods on the
KITTI-2012 and KITTI-2015 benchmarks, and also achieves state-of-the-art
performance on ETH3D. Code is available at
https://github.com/MiaoJieF/MC-Stereo.
