We present a unified formulation and model for three motion and 3D perception tasks:
optical flow, rectified stereo matching and unrectified stereo depth estimation from posed …

Optical flow is an indispensable building block for various important computer vision tasks,
including motion estimation, object tracking, and disparity measurement. In this work, we …

Denoising diffusion probabilistic models have transformed image generation with their
impressive fidelity and diversity. We show that they also excel in estimating optical flow and …

We introduce SEA-RAFT, a more simple, efficient, and accurate RAFT for optical flow.
Compared with RAFT, SEA-RAFT is trained with a new loss (mixture of Laplace). It directly …

Optical flow estimation is a challenging problem remaining unsolved. Recent deep learning
based optical flow models have achieved considerable success. However, these models …

Optical flow is a fundamental method used for quantitative motion estimation on the image
plane. In the deep learning era, most works treat it as a task of'matching of features', learning …

Optical flow, or the estimation of motion fields from image sequences, is one of the
fundamental problems in computer vision. Unlike most pixel-wise tasks that aim at achieving …

Recent deep learning-based optical flow estimators have exhibited impressive performance
in generating local flows between consecutive frames. However, the estimation of long …

Optical flow estimation is a classical yet challenging task in computer vision. One of the
essential factors in accurately predicting optical flow is to alleviate occlusions between …

In this paper, we propose a novel framework for optical flow estimation that achieves a good
balance between performance and efficiency. Our approach involves disentangling global …