Deep learning (DL) has become one of the mainstream and effective methods for point
cloud analysis tasks such as detection, segmentation and classification. To reduce …

Deep representation learning is a ubiquitous part of modern computer vision. While
Euclidean space has been the de facto standard manifold for learning visual …

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 …

Abstract 3D shape generation from text is a fundamental task in 3D representation learning.
The text-shape pairs exhibit a hierarchical structure where a general text like" chair" covers …

Transfer-based adversarial attacks involve generating adversarial point clouds in surrogate
models and transferring them to other models to assess 3D model robustness. However …

Recent advances in point cloud completion make it possible to simultaneously recover
complete shapes and fine details from partial point clouds captured by professional 3D …

Tendon-driven continuum robots have been gaining popularity in medical applications due
to their ability to curve around complex anatomical structures, potentially reducing the …

Study is being conducted on training Generative Adversarial Networks (GANs) from 2D
datasets to generate 3D human body avatars. Numerous applications, such as virtual reality …

The rapid development of 3D acquisition technology has made it possible to obtain point
clouds of real-world terrains. However, due to limitations in sensor acquisition technology or …

Point cloud completion aims to recover accurate global geometry and preserve fine-grained
local details from partial point clouds. Conventional methods typically predict unseen points …