Deep learning-based intelligent vehicle perception has been developing prominently in
recent years to provide a reliable source for motion planning and decision making in …

Recently, several frameworks for self-supervised learning of 3D scene flow on point clouds
have emerged. Scene flow inherently separates every scene into multiple moving agents …

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 …

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 …

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 …

Scene flow estimation is a long-standing problem in computer vision, where the goal is to
find the 3D motion of a scene from its consecutive observations. Recently, there have been …

Multi-beam LiDAR sensors, as used on autonomous vehicles and mobile robots, acquire
sequences of 3D range scans (“frames”). Each frame covers the scene sparsely, due to …

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 …

In this paper, we study the problem of 3D object segmentation from raw point clouds. Unlike
all existing methods which usually require a large amount of human annotations for full …

Accurate object rearrangement from vision is a crucial problem for a wide variety of real-
world robotics applications in unstructured environments. We propose IFOR, Iterative Flow …