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.
A Representation Separation Perspective to Correspondences-free Unsupervised 3D Point Cloud Registration
Northwestern Polytechnical University（西北工业大学）；Baidu Research（百度研究院）；National Engineering Laboratory of Deep Learning Technology and Application（深度学习技术及应用国家工程实验室）
3D Unsupervised Region-Aware Registration Transformer
New York University Abu Dhabi（纽约大学阿布扎比分校）；NYU Multimedia and Visual Computing Lab（纽约大学多媒体与视觉计算实验室）；NYUAD Center for Artificial Intelligence and Robotics（纽约大学阿布扎比人工智能与机器人中心）
Occlusion-aware Non-Rigid Point Cloud Registration via Unsupervised Neural Deformation Correntropy
Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Overlap Bias Matching is Necessary for Point Cloud Registration
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件学院）；School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
