MATTER: Multiscale Attention for Registration Error Regression

2025-09-16

AI Summary

Point cloud registration (PCR) quality verification is critical for downstream tasks such as SLAM and object tracking; however, existing methods predominantly formulate it as a coarse-grained classification problem, failing to precisely quantify misalignment magnitude. This work pioneers a regression-based paradigm for PCR quality verification. We propose a learnable error estimation network that explicitly models point cloud geometry via multi-scale feature extraction and attention mechanisms, enhancing robustness under heterogeneous point densities. Key contributions include: (1) the first regression-oriented formulation of PCR quality verification, enabling fine-grained misalignment quantification; and (2) a novel multi-scale attention aggregation module that improves prediction accuracy and generalization across complex spatial distributions. Evaluated on multiple standard benchmarks, our method achieves state-of-the-art regression performance—reducing mean absolute error by up to 37% over prior art—and significantly enhances SLAM mapping fidelity and re-registration efficiency.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.,~{it 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.

Problem

• Regression-based PCR quality validation for fine-grained quantification
• Multiscale attention features for robust error estimation
• Improving mapping quality by guiding re-registration with error regression

Innovation

• Regression-based PCR quality validation
• Multiscale feature extraction with attention
• Accurate error estimation for heterogeneous densities