A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …

Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

A growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their
promise for enhanced safety, efficiency, and economic benefits. While previous surveys …

We propose and study a method called FLOT that estimates scene flow on point clouds. We
start the design of FLOT by noticing that scene flow estimation on point clouds reduces to …

Motion analysis is one of the most fundamental and challenging problems in the field of
computer vision, which can be widely applied in many areas, such as autonomous driving …

Before the deep learning revolution, many perception algorithms were based on runtime
optimization in conjunction with a strong prior/regularization penalty. A prime example of this …

We address the problem of scene flow: given a pair of stereo or RGB-D video frames,
estimate pixelwise 3D motion. We introduce RAFT-3D, a new deep architecture for scene …

In this paper, we propose PolyTransform, a novel instance segmentation algorithm that
produces precise, geometry-preserving masks by combining the strengths of prevailing …

Abstract 3D scene flow estimation from point clouds is a low-level 3D motion perception task
in computer vision. Flow embedding is a commonly used technique in scene flow estimation …

We propose a data-driven scene flow estimation algorithm exploiting the observation that
many 3D scenes can be explained by a collection of agents moving as rigid bodies. At the …