The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

In this paper, we propose an unsupervised domain adaptation method for deep point cloud
representation learning. To model the internal structures in target point clouds, we first …

Recent point cloud understanding research suffers from performance drops on unseen data,
due to the distribution shifts across different domains. While recent studies use Domain …

This paper considers the synthetic-to-real adaptation of point cloud semantic segmentation,
which aims to segment the real-world point clouds with only synthetic labels available …

Recently, point cloud domain adaptation (DA) practices have been implemented to improve
the generalization ability of deep learning models on point cloud data. However, variations …

The superiority of deep learning based point cloud representations relies on large-scale
labeled datasets, while the annotation of point clouds is notoriously expensive. One of the …

Active learning, a label-efficient paradigm, empowers models to interactively query an oracle
for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem …

Domain generalization (DG) aims at solving distribution shift problems in various scenes.
Existing approaches are based on Convolution Neural Networks (CNNs) or Vision …

In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …

Understanding point clouds captured from the real-world is challenging due to shifts in data
distribution caused by varying object scales, sensor angles, and self-occlusion. Prior works …