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.

翻译：点云配准（PCR）对于许多下游任务（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错位（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质量划分为少数几个类别。在本工作中，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精细的量化。我们通过多尺度特征提取和基于注意力的聚合机制，扩展了先前使用的错位相关特征。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。此外，当用于指导建图下游任务时，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，显著提升了建图质量。