A Curvature and Density‐based Generative Representation of Shapes

Author:

Ye Z.1ORCID,Umetani N.2,Igarashi T.2,Hoffmann T.1

Affiliation:

1. TU Munich Germany

2. The University of Tokyo Japan

Funder

Core Research for Evolutional Science and Technology

Deutsche Forschungsgemeinschaft

Publisher

Wiley

Subject

Computer Graphics and Computer-Aided Design

Reference51 articles.

1. [AAB*15] AbadiM. AgarwalA. BarhamP. BrevdoE. ChenZ. CitroC. CorradoG. S. DavisA. DeanJ. DevinM. GhemawatS. GoodfellowI. HarpA. IrvingG. IsardM. JiaY. JozefowiczR. KaiserL. KudlurM. LevenbergJ. ManéD. MongaR. MooreS. MurrayD. OlahC. SchusterM. ShlensJ. SteinerB. SutskeverI. TalwarK. TuckerP. VanhouckeV. VasudevanV. ViégasF. VinyalsO. WardenP. WattenbergM. WickeM. YuY. ZhengX.:TensorFlow: Large‐scale machine learning on heterogeneous systems 2015. Software available from tensorflow.org. URL:https://www.tensorflow.org/. Accessed: 2019‐01‐01

2. Learning representations and generative models for 3D point clouds;Achlioptas P.;Proceedings of the 35th International Conference on Machine Learning,2018

3. Isotropic surface remeshing

4. The wave kernel signature: A quantum mechanical approach to shape analysis

5. Geometric Deep Learning: Going beyond Euclidean data

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