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

One critical challenge in 6D object pose estimation from a single RGBD image is efficient
integration of two different modalities, ie, color and depth. In this work, we tackle this problem …

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

Robotic manipulation requires accurate perception of the environment, which poses a
significant challenge due to its inherent complexity and constantly changing nature. In this …

Object pose refinement is essential for robust object pose estimation. Previous work has
made significant progress towards instance-level object pose refinement. Yet category-level …

Object pose estimation is a fundamental computer vision task exploited in several robotics
and augmented reality applications. Many established approaches rely on predicting 2D-3D …

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

We propose a test-time adaptation for 6D object pose tracking that learns to adapt a pre-
trained model to track the 6D pose of novel objects. We consider the problem of 6D object …

We present Normal Field Learning (NFL), a robust yet practical solution to perceive 3D
layouts of transparent objects and grasp them quickly. Conventional input modalities for …

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 …