Transparent object perception is a rapidly developing research problem in artificial
intelligence. The ability to perceive transparent objects enables robots to achieve higher …

This paper proposes a new glass segmentation method utilizing paired RGB and thermal
images. Due to the large difference between the transmission property of visible light and …

Most existing methods for category-level pose estimation rely on object point clouds.
However, when considering transparent objects, depth cameras are usually not able to …

Transparent objects are commonly seen in indoor scenes but are hard to estimate.
Currently, commercial depth cameras face difficulties in estimating the depth of transparent …

In many binary segmentation tasks, most CNNs-based methods use a U-shape encoder-
decoder network as their basic structure. They ignore two key problems when the encoder …

Traditional monocular depth estimation assumes that all objects are reliably visible in the
RGB color domain. However, this is not always the case as more and more buildings are …

Ghosting effects typically appear on glass surfaces, as each piece of glass has two contact
surfaces causing two slightly offset layers of reflections. In this paper, we propose to take …

Estimating the depth of transparent objects is one of the well-known challenges of RGB-D
cameras due to the reflection and refraction effects. Previously, researchers propose to …

Reconstructing transparent objects using affordable RGB-D cameras is a persistent
challenge in robotic perception due to inconsistent appearances across views in the RGB …

Mirror and glass are ubiquitous materials in the 3D indoor living environment. However, the
existing vision system always tends to neglect or misdiagnose them since they always …