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 …

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 …

We present an end-to-end joint training framework that explicitly models 6-DoF motion of
multiple dynamic objects, ego-motion, and depth in a monocular camera setup without …

Multi-frame depth estimation generally achieves high accuracy relying on the multi-view
geometric consistency. When applied in dynamic scenes, eg, autonomous driving, this …

This paper considers the generic problem of dense alignment between two images, whether
they be two frames of a video, two widely different views of a scene, two paintings depicting …

Estimating the motion of the camera together with the 3D structure of the scene from a
monocular vision system is a complex task that often relies on the so-called scene rigidity …

We present a self-supervised learning framework to estimate the individual object motion
and monocular depth from video. We model the object motion as a 6 degree-of-freedom …

Moving object segmentation is a crucial task for autonomous vehicles as it can be used to
segment objects in a class agnostic manner based on their motion cues. It enables the …

Unsupervised domain adaptation (UDA) has been a potent technique to handle the lack of
annotations in the target domain particularly in semantic segmentation task. This study …

Identifying and segmenting moving objects from a moving monocular camera is difficult
when there is unknown camera motion, different types of object motions and complex scene …