6 citations · 11 across the 15 of their papers we have counts for
5 papers · 1 filter
ReFlow6D: Refraction-Guided Transparent Object 6D Pose Estimation via Intermediate Representation Learning
Hrishikesh Gupta, Stefan Thalhammer, Jean-Baptiste Weibel +2
Transparent objects are ubiquitous in daily life, making their perception and robotics manipulation important. However, they present a major challenge due to their distinct refract…
Diffusion Features for Zero-Shot 6DoF Object Pose Estimation
Bernd Von Gimborn, Philipp Ausserlechner, Markus Vincze +1
Zero-shot object pose estimation enables the retrieval of object poses from images without necessitating object-specific training. In recent approaches this is facilitated by visio…
From Words to Poses: Enhancing Novel Object Pose Estimation with Vision Language Models
Tessa Pulli, Stefan Thalhammer, Simon Schwaiger +1
Robots are increasingly envisioned to interact in real-world scenarios, where they must continuously adapt to new situations. To detect and grasp novel objects, zero-shot pose esti…
Improving 2D-3D Dense Correspondences with Diffusion Models for 6D Object Pose Estimation
Peter Hönig, Stefan Thalhammer, Markus Vincze
Estimating 2D-3D correspondences between RGB images and 3D space is a fundamental problem in 6D object pose estimation. Recent pose estimators use dense correspondence maps and Poi…
Shape-biased Texture Agnostic Representations for Improved Textureless and Metallic Object Detection and 6D Pose Estimation
Peter Hönig, Stefan Thalhammer, Jean-Baptiste Weibel +2
Recent advances in machine learning have greatly benefited object detection and 6D pose estimation. However, textureless and metallic objects still pose a significant challenge due…