This paper concerns the research problem of point cloud registration to find the rigid transformation to optimally align the source point set with the target one. Learning robust point cloud registration models with deep neural networks has emerged as a powerful paradigm, offering promising performance in predicting the global geometric transformation for a pair of point sets. Existing methods firstly leverage an encoder to regress a latent shape embedding, which is then decoded into a shape-conditioned transformation via concatenation-based conditioning. However, different regions of a 3D shape vary in their geometric structures which makes it more sense that we have a region-conditioned transformation instead of the shape-conditioned one. In this paper we present a \underline{R}egion-\underline{A}ware point cloud \underline{R}egistration, denoted as RAR, to predict transformation for pairwise point sets in the self-supervised learning fashion. More specifically, we develop a novel region-aware decoder (RAD) module that is formed with an implicit neural region representation parameterized by neural networks. The implicit neural region representation is learned with a self-supervised 3D shape reconstruction loss without the need for region labels. Consequently, the region-aware decoder (RAD) module guides the training of the region-aware transformation (RAT) module and region-aware weight (RAW) module, which predict the transforms and weights for different regions respectively. The global geometric transformation from source point set to target one is then formed by the weighted fusion of region-aware transforms. Compared to the state-of-the-art approaches, our experiments show that our RAR achieves superior registration performance over various benchmark datasets (e.g. ModelNet40).

翻译：本文研究点云配准问题，旨在寻找最优刚体变换以将源点集与目标点集对齐。基于深度神经网络学习鲁棒的点云配准模型已成为一种强大范式，在预测点集对的全局几何变换方面展现出优异性能。现有方法首先利用编码器回归隐式形状嵌入，再通过基于拼接的条件化方式将其解码为形状条件变换。然而，三维形状的不同区域具有各异几何结构，这使得区域条件变换比形状条件变换更具合理性。本文提出区域感知点云配准方法（Region-Aware Registration，简称RAR），以自监督学习方式预测点集对间的变换。具体而言，我们开发了一种新颖的区域感知解码器（RAD）模块，该模块采用由神经网络参数化的隐式神经区域表示。这种隐式神经区域表示通过自监督三维形状重建损失进行学习，无需区域标签。由此，区域感知解码器（RAD）模块引导区域感知变换（RAT）模块与区域感知权重（RAW）模块的训练，分别预测不同区域的变换与权重。源点集到目标点集的全局几何变换由区域感知变换的加权融合构成。实验结果表明，与现有最优方法相比，我们的RAR在多个基准数据集（如ModelNet40）上均取得更优的配准性能。