Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

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 …

While current 3D object recognition research mostly focuses on the real-time, onboard
scenario, there are many offboard use cases of perception that are largely under-explored …

We propose a novel end-to-end deep scene flow model, called PointPWC-Net, that directly
processes 3D point cloud scenes with large motions in a coarse-to-fine fashion. Flow …

Due to the sparsity and irregularity of the point cloud data, methods that directly consume
points have become popular. Among all point-based models, graph convolutional networks …

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 …

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 …