Deep learning has emerged as a powerful tool in various domains, revolutionising machine
learning research. However, one persistent challenge is the scarcity of labelled training …

Radar is a key component of the suite of perception sensors used for safe and reliable
navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity …

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 …

Abstract 3D scene flow estimation aims to estimate point-wise motions between two
consecutive frames of point clouds. Superpoints, ie, points with similar geometric features …

A key challenge for autonomous vehicles is to navigate in unseen dynamic environments.
Separating moving objects from static ones is essential for navigation, pose estimation, and …

Accurate moving object segmentation is an es-sential task for autonomous driving. It can
provide effective information for many downstream tasks, such as collision avoidance, path …

Abstract Neural Scene Flow Prior (NSFP) is of significant interest to the vision community
due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with …

Understanding the scene is key for autonomously navigating vehicles, and the ability to
segment the surroundings online into moving and non-moving objects is a central ingredient …

This work proposes a novel approach to 4D radar-based scene flow estimation via cross-
modal learning. Our approach is motivated by the co-located sensing redundancy in modern …

In this work, we focus on scene flow learning on point clouds in a self-supervised manner. A
real-world scene can be well modeled as a collection of rigidly moving parts, therefore its …