D2
Computer Vision and Machine Learning

Keyang Zhou (PhD Student)

MSc Keyang Zhou

Adresse
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus
Standort
-
Telefon
+49 681 9325 2000
Fax
+49 681 9325 2099

Personal Information

Publications

Zhou, K. (2020). Unsupervised Shape and Pose Disentanglement for 3D Meshes. Universität des Saarlandes, Saarbrücken.
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BibTeX
@mastersthesis{ZhoMaster2020, TITLE = {Unsupervised Shape and Pose Disentanglement for {3D} Meshes}, AUTHOR = {Zhou, Keyang}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, }
Endnote
%0 Thesis %A Zhou, Keyang %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Unsupervised Shape and Pose Disentanglement for 3D Meshes : %G eng %U http://hdl.handle.net/21.11116/0000-0007-B432-5 %I Universität des Saarlandes %C Saarbrücken %D 2020 %P 59 p. %V master %9 master
Zhou, K., Bhatnagar, B. L., Schiele, B., & Pons-Moll, G. (2021). Adjoint Rigid Transform Network: Task-conditioned Alignment of 3D Shapes. Retrieved from https://arxiv.org/abs/2102.01161
(arXiv: 2102.01161)
Abstract
Most learning methods for 3D data (point clouds, meshes) suffer significant performance drops when the data is not carefully aligned to a canonical orientation. Aligning real world 3D data collected from different sources is non-trivial and requires manual intervention. In this paper, we propose the Adjoint Rigid Transform (ART) Network, a neural module which can be integrated with a variety of 3D networks to significantly boost their performance. ART learns to rotate input shapes to a learned canonical orientation, which is crucial for a lot of tasks such as shape reconstruction, interpolation, non-rigid registration, and latent disentanglement. ART achieves this with self-supervision and a rotation equivariance constraint on predicted rotations. The remarkable result is that with only self-supervision, ART facilitates learning a unique canonical orientation for both rigid and nonrigid shapes, which leads to a notable boost in performance of aforementioned tasks. We will release our code and pre-trained models for further research.
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BibTeX
@online{Zhou2102.01161, TITLE = {Adjoint Rigid Transform Network: {T}ask-conditioned Alignment of {3D} Shapes}, AUTHOR = {Zhou, Keyang and Bhatnagar, Bharat Lal and Schiele, Bernt and Pons-Moll, Gerard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2102.01161}, EPRINT = {2102.01161}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Most learning methods for 3D data (point clouds, meshes) suffer significant performance drops when the data is not carefully aligned to a canonical orientation. Aligning real world 3D data collected from different sources is non-trivial and requires manual intervention. In this paper, we propose the Adjoint Rigid Transform (ART) Network, a neural module which can be integrated with a variety of 3D networks to significantly boost their performance. ART learns to rotate input shapes to a learned canonical orientation, which is crucial for a lot of tasks such as shape reconstruction, interpolation, non-rigid registration, and latent disentanglement. ART achieves this with self-supervision and a rotation equivariance constraint on predicted rotations. The remarkable result is that with only self-supervision, ART facilitates learning a unique canonical orientation for both rigid and nonrigid shapes, which leads to a notable boost in performance of aforementioned tasks. We will release our code and pre-trained models for further research.}, }
Endnote
%0 Report %A Zhou, Keyang %A Bhatnagar, Bharat Lal %A Schiele, Bernt %A Pons-Moll, Gerard %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Adjoint Rigid Transform Network: Task-conditioned Alignment of 3D Shapes : %G eng %U http://hdl.handle.net/21.11116/0000-0009-80FA-C %U https://arxiv.org/abs/2102.01161 %D 2021 %X Most learning methods for 3D data (point clouds, meshes) suffer significant performance drops when the data is not carefully aligned to a canonical orientation. Aligning real world 3D data collected from different sources is non-trivial and requires manual intervention. In this paper, we propose the Adjoint Rigid Transform (ART) Network, a neural module which can be integrated with a variety of 3D networks to significantly boost their performance. ART learns to rotate input shapes to a learned canonical orientation, which is crucial for a lot of tasks such as shape reconstruction, interpolation, non-rigid registration, and latent disentanglement. ART achieves this with self-supervision and a rotation equivariance constraint on predicted rotations. The remarkable result is that with only self-supervision, ART facilitates learning a unique canonical orientation for both rigid and nonrigid shapes, which leads to a notable boost in performance of aforementioned tasks. We will release our code and pre-trained models for further research. %K Computer Science, Computer Vision and Pattern Recognition, cs.CV