MATTER: Multiscale Attention for Registration Error Regression

• URL: http://arxiv.org/abs/2509.12924v2
• Date: Thu, 18 Sep 2025 11:23:30 GMT
• Title: MATTER: Multiscale Attention for Registration Error Regression
• Authors: Shipeng Liu, Ziliang Xiong, Khac-Hoang Ngo, Per-Erik Forssén,
• Abstract summary: Point cloud registration (PCR) is crucial for many downstream tasks, such as simultaneous localization and mapping (SLAM) and object tracking.<n>All existing methods treat validation as a classification task, aiming to assign the quality to a few classes.<n>In this work, we instead use regression for PCR validation, allowing for a more fine-grained quantification of the registration quality.
• Score: 7.4240584069677995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point cloud registration (PCR) is crucial for many downstream tasks, such as simultaneous localization and mapping (SLAM) and object tracking. This makes detecting and quantifying registration misalignment, i.e., PCR quality validation, an important task. All existing methods treat validation as a classification task, aiming to assign the PCR quality to a few classes. In this work, we instead use regression for PCR validation, allowing for a more fine-grained quantification of the registration quality. We also extend previously used misalignment-related features by using multiscale extraction and attention-based aggregation. This leads to accurate and robust registration error estimation on diverse datasets, especially for point clouds with heterogeneous spatial densities. Furthermore, when used to guide a mapping downstream task, our method significantly improves the mapping quality for a given amount of re-registered frames, compared to the state-of-the-art classification-based method.

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