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.

翻译：点云配准（PCR）对许多下游任务（如同时定位与建图（SLAM）和目标跟踪）至关重要。这使得检测和量化配准失准（即PCR质量验证）成为一项重要任务。现有方法均将验证视为分类任务，旨在将PCR质量划分为少数类别。本工作中，我们转而采用回归方法进行PCR验证，从而实现对配准质量更细粒度的量化。此外，我们通过多尺度特征提取和基于注意力的特征聚合，扩展了先前使用的失准相关特征。这使得我们能够在多样化数据集上实现准确且稳健的配准误差估计，尤其适用于具有异质空间密度的点云。进一步地，当用于指导下游建图任务时，与当前最先进的基于分类的方法相比，我们的方法在给定重配准帧数的情况下，能显著提升建图质量。