Despite the tremendous progress of Masked Autoencoders (MAE) in developing vision tasks
such as image and video, exploring MAE in large-scale 3D point clouds remains …

Scene flow characterizes the 3D motion between two LiDAR scans captured by an
autonomous vehicle at nearby timesteps. Prevalent methods consider scene flow as point …

Masked autoencoding has become a successful pretraining paradigm for Transformer
models for text, images, and, recently, point clouds. Raw automotive datasets are suitable …

Learning 3D scene flow from LiDAR point clouds presents significant difficulties including
poor generalization from synthetic datasets to real scenes scarcity of real-world 3D labels …

Contrastive learning has recently demonstrated great potential for unsupervised pre-training
in 3D scene understanding tasks. However, most existing work randomly selects point …

Point cloud data has been extensively studied due to its compact form and flexibility in
representing complex 3D structures. The ability of point cloud data to accurately capture and …

The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD)
have attracted widespread attention due to their outstanding performance and the ability to …

Self-supervised pretraining on large unlabeled datasets has shown tremendous success on
improving the task performance of many computer vision tasks. However, such techniques …

Abstract 3D traffic scene comprises various 3D information about car objects, including their
pose and shape. However, most recent studies pay relatively less attention to reconstructing …

In this article, we investigate self-supervised 3D scene flow estimation and class-agnostic
motion prediction on point clouds. A realistic scene can be well modeled as a collection of …