no code implementations • 17 Apr 2023 • Chengzhi Wu, Junwei Zheng, Julius Pfrommer, Jürgen Beyerer
Modeling a 3D volumetric shape as an assembly of decomposed shape parts is much more challenging, but semantically more valuable than direct reconstruction from a full shape representation.
1 code implementation • CVPR 2023 • Chengzhi Wu, Junwei Zheng, Julius Pfrommer, Jürgen Beyerer
Point cloud sampling is a less explored research topic for this data representation.
Ranked #31 on 3D Point Cloud Classification on ModelNet40
no code implementations • 12 Jan 2023 • Chengzhi Wu, Xuelei Bi, Julius Pfrommer, Alexander Cebulla, Simon Mangold, Jürgen Beyerer
On robotics computer vision tasks, generating and annotating large amounts of data from real-world for the use of deep learning-based approaches is often difficult or even impossible.
no code implementations • 11 Jan 2023 • Chengzhi Wu, Linxi Qiu, Kanran Zhou, Julius Pfrommer, Jürgen Beyerer
Our work is a sub-task in the framework of a remanufacturing project, in which small electric motors are used as fundamental objects.
no code implementations • 11 Jan 2023 • Chengzhi Wu, Kanran Zhou, Jan-Philipp Kaiser, Norbert Mitschke, Jan-Felix Klein, Julius Pfrommer, Jürgen Beyerer, Gisela Lanza, Michael Heizmann, Kai Furmans
To enable automatic disassembly of different product types with uncertain conditions and degrees of wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements are needed.
no code implementations • 11 Jan 2023 • Chengzhi Wu, Julius Pfrommer, Jürgen Beyerer, Kangning Li, Boris Neubert
We present an improved approach for 3D object detection in point cloud data based on the Frustum PointNet (F-PointNet).
no code implementations • 11 Jan 2023 • Chengzhi Wu, Julius Pfrommer, Mingyuan Zhou, Jürgen Beyerer
We propose a combined generative and contrastive neural architecture for learning latent representations of 3D volumetric shapes.
1 code implementation • 29 Mar 2019 • Laura von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, Michal Walczak, Jochen Garcke, Christian Bauckhage, Jannis Schuecker
It considers the source of knowledge, its representation, and its integration into the machine learning pipeline.