MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching

• URL: http://arxiv.org/abs/2311.02340v2
• Date: Sat, 27 Jan 2024 10:49:02 GMT
• Title: MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching
• Authors: Miaojie Feng, Junda Cheng, Hao Jia, Longliang Liu, Gangwei Xu, Qingyong Hu, Xin Yang
• Abstract summary: We present a novel iterative optimization architecture called MC-Stereo. It mitigates the multi-peak distribution problem in matching through the multi-peak lookup strategy. It integrates the coarse-to-fine concept into the iterative framework via the cascade search range. MC-Stereo ranks first among all publicly available methods on the KITTI-2012 and KITTI-2015 benchmarks.
• Score: 15.786593303130477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, 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.

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