2 papers
cs.CV2024
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…
cs.CV2024
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…