Zero-shot 3D point cloud understanding can be achieved via 2D Vision-Language Models
(VLMs). Existing strategies directly map VLM representations from 2D pixels of rendered or …

Inliers estimation constitutes a pivotal step in partially overlapping point cloud registration.
Existing methods broadly obey coordinate-based scheme where inlier confidence is scored …

Point cloud registration (PCR) involves determining a rigid transformation that aligns one
point cloud to another. Despite the plethora of outstanding deep learning (DL)-based …

Point cloud registration is a crucial task in computer vision and 3D reconstruction, aiming to
align multiple point clouds to achieve globally consistent geometric structures. However …

Point cloud registration is a critical task in various 3D applications. Supervised approaches
are restricted by the difficulty and cost of acquiring ground-truth annotations. Thus …

Registration of point clouds collected from a pair of distant vehicles provides a
comprehensive and accurate 3D view of the driving scenario which is vital for driving safety …

Data plays a crucial role in training learning-based methods for 3D point cloud registration.
However, the real-world dataset is expensive to build, while rendering-based synthetic data …

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 …

Image-to-point cloud registration aligns the data acquired by heterogeneous sensors such
as camera and LiDAR. To capture cross-modal correspondences for estimating the relative …

As a fundamental problem in computer vision, point cloud registration aims to seek the
optimal transformation for aligning a pair of point clouds. In most existing methods, the …