Existing point cloud registration models suffer from large performance loss in low-overlap
scenarios, while the generalization ability of most models are weak. In this article, we design …

Estimating the 6D pose of objects unseen during training is highly desirable yet challenging.
Zero-shot object 6D pose estimation methods address this challenge by leveraging …

Extracting geometric features from 3D point clouds is widely applied in many tasks, including
registration and recognition. We propose a simple yet effective method, termed height …

Partial point cloud registration is a challenging problem in robotics, especially when the
robot undergoes a large transformation, causing a significant initial pose error and a low …

Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …

A single receptive field limits the expression of multilevel receptive field features in point
cloud registration, leading to the pseudo-matching of objects with similar geometric …

Low-overlap registration is an important subtask in point cloud registration. In this letter, we
focus on two extreme states of low-overlap registration tasks: zero overlap rate and even …

Partial point cloud registration is an important step in generating a full 3D model. Many deep
learning‐based methods show good performance for the registration of complete point …

How to accurately register partial point cloud still remains a challenging task, because of its
irregular and unordered structure in a non-Euclidean space, noise, outliers, and other …

Feature-based point cloud registration algorithms have gained more attention recently for
their high robustness. Outlier rejection is a key step of such algorithms. With the …