@inproceedings{b5f6bf710375443f8b1d8ea4bba34e44,
title = "Structure-aware shape synthesis",
abstract = "We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function. Complex 3D shapes often can be summarized using a coarsely defined structure which is consistent and robust across variety of observations. However, existing synthesis techniques do not account for structure during training, and thus often generate implausible and structurally unrealistic shapes. During training, we enforce structural constraints in order to enforce consistency and structure across the entire manifold. We propose a novel methodology for training 3D generative models that incorporates structural information into an end-to-end training pipeline.",
keywords = "3d shape synthesis, Neural networks, Shape modelling",
author = "Elena Balashova and Vivek Singh and Jiangping Wang and Brian Teixeira and Terrence Chen and Thomas Funkhouser",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 6th International Conference on 3D Vision, 3DV 2018 ; Conference date: 05-09-2018 Through 08-09-2018",
year = "2018",
month = oct,
day = "12",
doi = "10.1109/3DV.2018.00026",
language = "English (US)",
series = "Proceedings - 2018 International Conference on 3D Vision, 3DV 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "140--149",
booktitle = "Proceedings - 2018 International Conference on 3D Vision, 3DV 2018",
address = "United States",
}