5 papers
Self6D: Self-Supervised Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Jianzhun Shao +3
6D object pose estimation is a fundamental problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliable 6D pose estima…
CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning
Fabian Manhardt, Gu Wang, Benjamin Busam +5
Contemporary monocular 6D pose estimation methods can only cope with a handful of object instances. This naturally hampers possible applications as, for instance, robots seamlessly…
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape
Fabian Manhardt, Wadim Kehl, Adrien Gaidon
We present a deep learning method for end-to-end monocular 3D object detection and metric shape retrieval. We propose a novel loss formulation by lifting 2D detection, orientation,…
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data
Fabian Manhardt, Diego Martin Arroyo, Christian Rupprecht +4
3D object detection and pose estimation from a single image are two inherently ambiguous problems. Oftentimes, objects appear similar from different viewpoints due to shape symmetr…
SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again
Wadim Kehl, Fabian Manhardt, Federico Tombari +2
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover th…