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 …

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 …

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 …

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 …

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 …

Stereo matching estimates the disparity between a rectified image pair, which is of great
importance to depth sensing, autonomous driving, and other related tasks. Previous works …

This paper presents HITNet, a novel neural network architecture for real-time stereo
matching. Contrary to many recent neural network approaches that operate on a full …

We present a novel method for simultaneous learning of depth, egomotion, object motion,
and camera intrinsics from monocular videos, using only consistency across neighboring …