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 …

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 …

Despite impressive performance for high-level downstream tasks, self-supervised pre-
training methods have not yet fully delivered on dense geometric vision tasks such as stereo …

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 …

To apply optical flow in practice, it is often necessary to resize the input to smaller
dimensions in order to reduce computational costs. However, downsizing inputs makes the …

In this work we introduce ProMotion a unified prototypical transformer-based framework
engineered to model fundamental motion tasks. ProMotion offers a range of compelling …

Optical flow is a classical task that is important to the vision community. Classical optical flow
estimation uses two frames as input whilst some recent methods consider multiple frames to …

Recent advancements in neural network-based optical flow estimation often come with
prohibitively high computational and memory requirements, presenting challenges in their …

Recent unsupervised multi-object detection models have shown impressive performance
improvements, largely attributed to novel architectural inductive biases. Unfortunately …