1 citations · 1 across the 2 of their papers we have counts for
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Every9D-21M: Large-Scale Real-World 9D Canonicalization of Everyday Objects
Leonhard Sommer, Emil Akopyan, Adam Kortylewski
Estimating the 9D pose of everyday objects from a single real-world image remains challenging. This is largely due to the lack of large-scale supervision. Most existing datasets ei…
Category-Level 3D Correspondence in Camera Space via Morphable Object Priors
Leonhard Sommer, Artur Jesslen, Basavaraj Sunagad +1
Understanding 3D objects from images is fundamental to robotics and AR/VR applications. While recent work has made progress in category-level pose estimation, current representatio…
Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space
Leonhard Sommer, Olaf Dünkel, Christian Theobalt +1
3D morphable models (3DMMs) are a powerful tool to represent the possible shapes and appearances of an object category. Given a single test image, 3DMMs can be used to solve variou…
Unsupervised Learning of Category-Level 3D Pose from Object-Centric Videos
Leonhard Sommer, Artur Jesslen, Eddy Ilg +1
Category-level 3D pose estimation is a fundamentally important problem in computer vision and robotics, e.g. for embodied agents or to train 3D generative models. However, so far m…
SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection
Leonhard Sommer, Philipp Schröppel, Thomas Brox
We propose SF2SE3, a novel approach to estimate scene dynamics in form of a segmentation into independently moving rigid objects and their SE(3)-motions. SF2SE3 operates on two con…