Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …

Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between
point clouds in point cloud completion, as well as a loss function for (deep) learning …

Anatomical consistency in biomarker segmentation is crucial for many medical image
analysis tasks. A promising paradigm for achieving anatomically consistent segmentation …

Point cloud completion aims to recover the complete shape based on a partial observation.
Existing methods require either complete point clouds or multiple partial observations of the …

Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-
to-sample methods have recently received increasing attention from the community for …

Due to the unsatisfactory performance of supervised methods on unpaired real-world scans,
point cloud completion via cross-domain adaptation has recently drawn growing attention …

Most real-world 3D measurements from depth sensors are incomplete, and to address this
issue the point cloud completion task aims to predict the complete shapes of objects from …

In this letter, we introduce ViHOPE, a novel framework for estimating the 6D pose of an in-
hand object using visuotactile perception. Our key insight is that the accuracy of the 6D …

A point cloud is a discrete set of data points sampled from a 3D geometric surface. Chamfer
distance (CD) is a popular metric and training loss to measure the distances between point …

Adaptable models could greatly benefit robotic agents operating in the real world, allowing
them to deal with novel and varying conditions. While approaches such as Bayesian …