用马氏k近邻提升点云配准的鲁棒性，适配多种方法。
Mahalanobis k-NN: A Statistical Lens for Robust Point-Cloud Registrations
- 基于马氏k近邻捕捉局部分布与表面几何特征。
- 在ModelNet40和FAUST上显著提升配准精度，少样本分类准确率提升约20%。
- 首次证明注册所得特征具判别力，适合点云分析研究者。
本文提出马氏k近邻（Mahalanobis k-NN）：一种用于解决学习型点云配准中任意密度点云下特征匹配挑战的统计视角。通过利用马氏k近邻固有的局部邻域分布与表面几何捕获能力，该方法可无缝集成至任意基于局部图的点云分析框架中。本文聚焦于Deep Closest Point（DCP）与Deep Universal Manifold Embedding（DeepUME）两种方法。在ModelNet40和FAUST数据集上的广泛基准测试表明，所提方法在点云配准任务中表现优异。此外，我们首次证明，通过点云配准获得的特征本身具备判别能力，在ModelNet40和ScanObjectNN上的少样本分类任务中平均准确率提升了约20%。
原文摘要 · Abstract (English)
In this paper, we discuss Mahalanobis k-NN: A Statistical Lens designed to address the challenges of feature matching in learning-based point cloud registration when confronted with an arbitrary density of point clouds. We tackle this by adopting Mahalanobis k-NN's inherent property to capture the distribution of the local neighborhood and surficial geometry. Our method can be seamlessly integrated into any local-graph-based point cloud analysis method. In this paper, we focus on two distinct methodologies: Deep Closest Point (DCP) and Deep Universal Manifold Embedding (DeepUME). Our extensive benchmarking on the ModelNet40 and FAUST datasets highlights the efficacy of the proposed method in point cloud registration tasks. Moreover, we establish for the first time that the features acquired through point cloud registration inherently can possess discriminative capabilities. This is evident by a substantial improvement of about 20% in the average accuracy observed in the point cloud few-shot classification task, benchmarked on ModelNet40 and ScanObjectNN.
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