Inspired by the human cognitive system, attention is a mechanism that imitates the human
cognitive awareness about specific information, amplifying critical details to focus more on …

Given the rapid development of 3D scanners, point clouds are becoming popular in AI-
driven machines. However, point cloud data is inherently sparse and irregular, causing …

Transformers have been at the heart of the Natural Language Processing (NLP) and
Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring …

We introduce PartGlot, a neural framework and associated architectures for learning
semantic part segmentation of 3D shape geometry, based solely on part referential …

Point cloud is widely available in the manufacturing system with the continuous
development of 3-D sensors. Accurate point cloud segmentation can automatically identify …

The PointPillars algorithm can detect vehicles, pedestrians, and cyclists on the road, and is
widely used in the field of environmental awareness in autonomous driving. However, its …

Traditional neural networks have the disadvantages of over-reliance on hardware resources
and high requirements for application equipment performance. Therefore, they cannot be …

Unsupervised point cloud completion aims to infer the whole geometry of a partial object
observation without requiring partial-complete correspondence. Differing from existing …

Point cloud registration is widely used in autonomous driving, SLAM, and 3D reconstruction,
and it aims to align point clouds from different viewpoints or poses under the same …

With the rapid development of deep learning, encoded objects such as images, videos, and
point cloud objects are increasingly used in downstream tasks optimized by deep learning …