With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …

Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

Abstract Recurrent All-Pairs Field Transforms (RAFT) has shown great potentials in
matching tasks. However, all-pairs correlations lack non-local geometry knowledge and …

Learning-based optical flow estimation has been dominated with the pipeline of cost volume
with convolutions for flow regression, which is inherently limited to local correlations and …

With the advent of convolutional neural networks, stereo matching algorithms have recently
gained tremendous progress. However, it remains a great challenge to accurately extract …

Stereo matching is a fundamental building block for many vision and robotics applications.
An informative and concise cost volume representation is vital for stereo matching of high …

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 RAFT-Stereo, a new deep architecture for rectified stereo based on the optical
flow network RAFT [35]. We introduce multi-level convolutional GRUs, which more efficiently …

Prior work for articulated 3D shape reconstruction often relies on specialized multi-view and
depth sensors or pre-built deformable 3D models. Such methods do not scale to diverse sets …

Self-supervised monocular depth estimation networks are trained to predict scene depth
using nearby frames as a supervision signal during training. However, for many …