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 explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

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

Domain shifts such as sensor type changes and geographical situation variations are
prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on …

Unlocking the power of 3D point cloud machine learning models can be a challenge due to
the need for extensive labeled datasets, which presents a challenge when applying these …

Human motion prediction is crucial for human-centric multimedia understanding and
interacting. Current methods typically rely on ground truth human poses as observed input …

Multi-model fitting aims to robustly estimate the parameters of various model instances in
data contaminated by noise and outliers. Most previous works employ only a single type of …

Point cloud plays a significant role in recent learning-based vision tasks, which contain
additional information about the physical space compared to 2D images. However, such a …

As a fundamental task of 3D perception, point cloud recognition has shown significant
progress in recent years. However, existing methods still face challenges when dealing with …

Point clouds are a popular representation for 3D data, widely used in applications such as
autonomous driving, robotics, and computer graphics. Classifying objects within point clouds …