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

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

Test-time adaptation methods have been gaining attention recently as a practical solution for
addressing source-to-target domain gaps by gradually updating the model without requiring …

In this paper, we introduce neural texture learning for 6D object pose estimation from
synthetic data and a few unlabelled real images. Our major contribution is a novel learning …

Estimating 6D object poses is a major challenge in 3D computer vision. Building on
successful instance-level approaches research is shifting towards category-level pose …

6-DoF robotic gras** is a long-lasting but un-solved problem. Recent methods utilize
strong 3D networks to extract geometric gras** representations from depth sensors …

Recent advances in neural radiance fields enable novel view synthesis of photo-realistic
images in dynamic settings, which can be applied to scenarios with human animation …

Abstract 6D object pose estimation is crucial in the field of computer vision. However, it
suffers from a significant lack of large-scale and diverse datasets, impeding comprehensive …

We present the HANDAL dataset for category-level object pose estimation and affordance
prediction. Unlike previous datasets, ours is focused on robotics-ready manipulable objects …

Light has many properties that vision sensors can passively measure. Colour-band
separated wavelength and intensity are arguably the most commonly used for monocular 6D …