Abstract We present Lepard, a Learning based approach for partial point cloud matching in
rigid and deformable scenes. The key characteristics are the following techniques that …

Learning-based perception and prediction modules in modern autonomous driving systems
typically rely on expensive human annotation and are designed to perceive only a handful of …

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 …

Scene perception and trajectory forecasting are two fundamental challenges that are crucial
to a safe and reliable autonomous driving (AD) system. However, most proposed methods …

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 …

We study the problem of outlier correspondence pruning for non-rigid point cloud
registration. In rigid registration, spatial consistency has been a commonly used criterion to …

Recent years have witnessed significant progress in the field of neural surface
reconstruction. While extensive focus was put on volumetric and implicit approaches, a …

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 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 …