The emerging topic of cross-source point cloud (CSPC) registration has attracted increasing
attention with the fast development background of 3D sensor technologies. Different from the …

Point cloud registration is the process of aligning point clouds collected at different locations
of the same scene, which transforms the data into a common coordinate system and forms …

The existing state-of-the-art point descriptor relies on structure information only, which omits
the texture information. However, texture information is crucial for our humans to distinguish …

We present Point-TTA, a novel test-time adaptation framework for point cloud registration
(PCR) that improves the generalization and the performance of registration models. While …

Recently, 3D point cloud classification has made significant progress with the help of many
datasets. However, these datasets do not reflect the incomplete nature of real-world point …

Rejecting correspondence outliers enables to boost the correspondence quality, which is a
critical step in achieving high point cloud registration accuracy. The current state-of-the-art …

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 …

Recent years have witnessed a signifcant breakthrough in the 3D domain. To track the most
recent advances in the 3D field, in this paper, we provide a comprehensive survey of recent …

Point cloud registration plays a crucial role in processing large-scale building point cloud
data. However, existing registration algorithms face challenges in effectively handling …

Robust cross-source point cloud (CSPC) registration is a formidable task due to the huge
differences in point cloud data collected by multi-source sensors. We propose a point cloud …