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 …

Adapting to a continuously evolving environment is a safety-critical challenge inevitably
faced by all autonomous-driving systems. Existing image-and video-based driving datasets …

Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound
progress in stereo matching. However, most of these successes are limited to a specific …

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 …

Robust and accurate geometric understanding against adverse weather conditions is one
top prioritized conditions to achieve a high-level autonomy of self-driving cars. However …

Abstract Zero-shot Video Object Segmentation (ZSVOS) aims at segmenting the primary
moving object without any human annotations. Mainstream solutions mainly focus on …

We propose a cost volume-based neural network for depth inference from multi-view
images. We demonstrate that building a cost volume pyramid in a coarse-to-fine manner …

Estimating depth from RGB images is a long-standing ill-posed problem, which has been
explored for decades by the computer vision, graphics, and machine learning communities …

Deep learning-based stereo matching methods have made remarkable progress in recent
years. However, it is still a challenging task to achieve high accuracy in real time. In …