CasP: Improving Semi-Dense Feature Matching Pipeline Leveraging Cascaded Correspondence Priors for Guidance

• URL: http://arxiv.org/abs/2507.17312v2
• Date: Fri, 01 Aug 2025 06:39:06 GMT
• Title: CasP: Improving Semi-Dense Feature Matching Pipeline Leveraging Cascaded Correspondence Priors for Guidance
• Authors: Peiqi Chen, Lei Yu, Yi Wan, Yingying Pei, Xinyi Liu, Yongxiang Yao, Yingying Zhang, Lixiang Ru, Liheng Zhong, Jingdong Chen, Ming Yang, Yongjun Zhang,
• Abstract summary: We propose a novel pipeline, CasP, which leverages cascaded correspondence priors for guidance.<n>In the second phase, one-to-one matches are determined by restricting the search range to the one-to-many prior areas.<n>This pipeline benefits from incorporating high-level features, which helps reduce the computational costs of low-level feature extraction.
• Score: 38.25767390254383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-dense feature matching methods have shown strong performance in challenging scenarios. However, the existing pipeline relies on a global search across the entire feature map to establish coarse matches, limiting further improvements in accuracy and efficiency. Motivated by this limitation, we propose a novel pipeline, CasP, which leverages cascaded correspondence priors for guidance. Specifically, the matching stage is decomposed into two progressive phases, bridged by a region-based selective cross-attention mechanism designed to enhance feature discriminability. In the second phase, one-to-one matches are determined by restricting the search range to the one-to-many prior areas identified in the first phase. Additionally, this pipeline benefits from incorporating high-level features, which helps reduce the computational costs of low-level feature extraction. The acceleration gains of CasP increase with higher resolution, and our lite model achieves a speedup of $\sim2.2\times$ at a resolution of 1152 compared to the most efficient method, ELoFTR. Furthermore, extensive experiments demonstrate its superiority in geometric estimation, particularly with impressive cross-domain generalization. These advantages highlight its potential for latency-sensitive and high-robustness applications, such as SLAM and UAV systems. Code is available at https://github.com/pq-chen/CasP.

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