Multi-Order Matching Network for Alignment-Free Depth Super-Resolution

• URL: http://arxiv.org/abs/2511.16361v1
• Date: Thu, 20 Nov 2025 13:44:51 GMT
• Title: Multi-Order Matching Network for Alignment-Free Depth Super-Resolution
• Authors: Zhengxue Wang, Zhiqiang Yan, Yuan Wu, Guangwei Gao, Xiang Li, Jian Yang,
• Abstract summary: In this paper, we propose a novel alignment-free framework that adaptively retrieves and selects the most relevant information from misaligned RGB.<n>Experiments demonstrate that MOMNet achieves state-of-the-art performance and exhibits outstanding robustness.
• Score: 29.19515140577684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent guided depth super-resolution methods are premised on the assumption of strictly spatial alignment between depth and RGB, achieving high-quality depth reconstruction. However, in real-world scenarios, the acquisition of strictly aligned RGB-D is hindered by inherent hardware limitations (e.g., physically separate RGB-D sensors) and unavoidable calibration drift induced by mechanical vibrations or temperature variations. Consequently, existing approaches often suffer inevitable performance degradation when applied to misaligned real-world scenes. In this paper, we propose the Multi-Order Matching Network (MOMNet), a novel alignment-free framework that adaptively retrieves and selects the most relevant information from misaligned RGB. Specifically, our method begins with a multi-order matching mechanism, which jointly performs zero-order, first-order, and second-order matching to comprehensively identify RGB information consistent with depth across multi-order feature spaces. To effectively integrate the retrieved RGB and depth, we further introduce a multi-order aggregation composed of multiple structure detectors. This strategy uses multi-order priors as prompts to facilitate the selective feature transfer from RGB to depth. Extensive experiments demonstrate that MOMNet achieves state-of-the-art performance and exhibits outstanding robustness.

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