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

Transparent objects are a common part of everyday life, yet they possess unique visual
properties that make them incredibly difficult for standard 3D sensors to produce accurate …

Light field (LF) depth estimation is a crucial basis for LF-related applications. Most existing
methods are based on the Lambertian assumption and cannot deal with non-Lambertian …

Segmentation of transparent objects is a hard, open problem in computer vision.
Transparent objects lack texture of their own, adopting instead the texture of scene …

This study reviews the recent advances in data-driven polarimetric imaging technologies
based on a wide range of practical applications. The widespread international research and …

The segmentation of transparent objects can be very useful in computer vision applications.
However, because they borrow texture from their background and have a similar …

6DoF object pose estimation is a foundation for many important applications, such as robotic
grasping, automatic driving, and so on. However, it is very challenging to estimate 6DoF …

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 …

Perception of transparent objects has been an open challenge in robotics despite advances
in sensors and datadriven learning approaches. In this paper, we introduce a new approach …

Recently RGB-D sensors have become very popular in the area of Simultaneous
Localisation and Mapping (SLAM). The RGB-D SLAM approach relies heavily on the …