Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

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 …

In current decades, significant advancements in robotics engineering and autonomous
vehicles have improved the requirement for precise depth measurements. Depth estimation …

Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

Depth estimation and scene parsing are two particularly important tasks in visual scene
understanding. In this paper we tackle the problem of simultaneous depth estimation and …

Monocular depth estimation is a challenging task in scene understanding, with the goal to
acquire the geometric properties of 3D space from 2D images. Due to the lack of RGB-depth …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

As an alternative to manual pixel-wise annotation, synthetic data has been increasingly
used for training semantic segmentation models. Such synthetic images and semantic labels …

The Ken Burns effect allows animating still images with a virtual camera scan and zoom.
Adding parallax, which results in the 3D Ken Burns effect, enables significantly more …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …