Point cloud registration is the process of aligning a pair of point sets via searching for a geometric transformation. Recent works leverage the power of deep learning for registering a pair of point sets. However, unfortunately, deep learning models often require a large number of ground truth labels for training. Moreover, for a pair of source and target point sets, existing deep learning mechanisms require explicitly designed encoders to extract both deep spatial features from unstructured point clouds and their spatial correlation representation, which is further fed to a decoder to regress the desired geometric transformation for point set alignment. To further enhance deep learning models for point set registration, this paper proposes Deep-3DAligner, a novel unsupervised registration framework based on a newly introduced deep Spatial Correlation Representation (SCR) feature. The SCR feature describes the geometric essence of the spatial correlation between source and target point sets in an encoding-free manner. More specifically, our method starts with optimizing a randomly initialized latent SCR feature, which is then decoded to a geometric transformation (i.e., rotation and translation) to align source and target point sets. Our Deep-3DAligner jointly updates the SCR feature and weights of the transformation decoder towards the minimization of an unsupervised alignment loss. We conducted experiments on the ModelNet40 datasets to validate the performance of our unsupervised Deep-3DAligner for point set registration. The results demonstrated that, even without ground truth and any assumption of a direct correspondence between source and target point sets for training, our proposed approach achieved comparative performance compared to most recent supervised state-of-the-art approaches.

中文翻译：

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3D 点集配准的无监督学习

点云配准是通过搜索几何变换来对齐一对点集的过程。最近的工作利用深度学习的力量来注册一对点集。然而，不幸的是，深度学习模型通常需要大量的真实标签进行训练。此外，对于一对源点集和目标点集，现有的深度学习机制需要明确设计的编码器从非结构化点云中提取深度空间特征及其空间相关表示，然后将其进一步馈送到解码器以回归所需的几何变换点集对齐。为了进一步增强点集配准的深度学习模型，本文提出了 Deep-3DAligner，基于新引入的深度空间相关表示 (SCR) 功能的新型无监督注册框架。SCR 特征以无编码的方式描述了源点集和目标点集之间空间相关性的几何本质。更具体地说，我们的方法从优化随机初始化的潜在 SCR 特征开始，然后将其解码为几何变换（即旋转和平移）以对齐源点集和目标点集。我们的 Deep-3DAligner 联合更新变换解码器的 SCR 特征和权重，以最小化无监督对齐损失。我们在 ModelNet40 数据集上进行了实验，以验证我们的无监督 Deep-3DAligner 在点集注册方面的性能。结果表明，