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Leonhard Sommer

2 accepted papers

2025

Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space

CVPR 2025poster

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 various tasks, such as predicting the 3D shape, pose, semantic correspondence, and instance segmentation of an object. Unfortu…

2024

Unsupervised Learning of Category-Level 3D Pose from Object-Centric Videos

CVPR 2024poster

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 methods that estimate the category-level object pose require either large amounts of human annotations CAD models or input…