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

Abstract We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network
architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D …

Stereo matching is one of the longest-standing problems in computer vision with close to 40
years of studies and research. Throughout the years the paradigm has shifted from local …

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 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 …

The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D
cost volumes to regularize and regress the output depth or disparity. These methods are …

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 …

To reduce the human efforts in neural network design, Neural Architecture Search (NAS)
has been applied with remarkable success to various high-level vision tasks such as …

Despite the remarkable progress made by learning based stereo matching algorithms, one
key challenge remains unsolved. Current state-of-the-art stereo models are mostly based on …