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

Object pose estimation is a fundamental computer vision problem with broad applications in
augmented reality and robotics. Over the past decade, deep learning models, due to their …

Object pose estimation is a core perception task that enables, for example, object
manipulation and scene understanding. The widely available, inexpensive, and high …

Shared perception between robotic systems significantly enhances their ability to
understand and interact with their environment, leading to improved performance and …

Accurate object pose estimation is crucial for robotic applications and recent trends in
category-level pose estimation show great potential for applications encountering a large …

We study the 3D object understanding task for manipulating everyday objects with different
material properties (diffuse, specular, transparent and mixed). Existing monocular and RGB …

Transparent objects are ubiquitous in daily life, making their perception and robotics
manipulation important. However, they present a major challenge due to their distinct …

Estimating the 6 degrees-of-freedom (6DoF) pose of an object from a single image is an
important task in computer vision. Many recent works have addressed it by establishing 2D …

Category-level pose estimation is proposed to predict the 6D pose of objects under a
specific category and has wide applications in fields such as robotics, virtual reality, and …

Object pose estimation is a prominent task in computer vision. The object pose gives the
orientation and translation of the object in real-world space, which allows various …