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 …

In the realm of autonomous driving, accurate 3D perception is the foundation. However,
developing such models relies on extensive human annotations–a process that is both …

Recently, some weakly supervised 3D point cloud segmentation methods have been
proposed to develop effective models with minimum annotation efforts. Our previous work …

Abstract 3D point cloud semantic segmentation, a pivotal task in fields such as autonomous
driving and urban planning, confronts the challenge of performance degradation under …

Cross-modal contrastive distillation has recently been explored for learning effective 3D
representations. However, existing methods focus primarily on modality-shared features …

Contrastive image-to-LiDAR knowledge transfer, commonly used for learning 3D
representations with synchronized images and point clouds, often faces a self-conflict …

Annotating lidar point clouds for autonomous driving is a notoriously expensive and time-
consuming task. In this work, we show that the quality of recent self-supervised lidar scan …

A common strategy to improve lidar segmentation results on rare semantic classes consists
of pasting objects from one lidar scene into another. While this augments the quantity of …

3D occupancy prediction (Occ) is a rapidly rising challenging perception task in the field of
autonomous driving which represents the driving scene as uniformly partitioned 3D voxel …

Test-time adaptation (TTA) adapts the pre-trained models during inference using unlabeled
test data and has received a lot of research attention due to its potential practical value …