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 …

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 …

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 …

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 …

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 …

Cross-source (CS) point cloud registration is a prerequisite for effectively leveraging the
complementary information of multiple 3-D sensors. However, existing point cloud …

Current methods for point cloud semantic segmentation depend on the extraction of
descriptive features. However, unlike images, point clouds are irregular and often lack …

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 …

Feature matching refers to building correct point correspondences from two-view images.
Typical methods create coarse matches merely based on the similarity of local descriptors …