Composite Shape Modeling via Latent Space Factorization

Anastasia Dubrovina, Fei Xia, Panos Achlioptas, Mira Shalah, Raphael Groscot, Leonidas J. Guibas; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 8140-8149

Abstract


We present a novel neural network architecture, termed Decomposer-Composer, for semantic structure-aware 3D shape modeling. Our method utilizes an auto-encoder-based pipeline, and produces a novel factorized shape embedding space, where the semantic structure of the shape collection translates into a data-dependent sub-space factorization, and where shape composition and decomposition become simple linear operations on the embedding coordinates. We further propose to model shape assembly using an explicit learned part deformation module, which utilizes a 3D spatial transformer network to perform an in-network volumetric grid deformation, and which allows us to train the whole system end-to-end. The resulting network allows us to perform part-level shape manipulation, unattainable by existing approaches. Our extensive ablation study, comparison to baseline methods and qualitative analysis demonstrate the improved performance of the proposed method.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Dubrovina_2019_ICCV,
author = {Dubrovina, Anastasia and Xia, Fei and Achlioptas, Panos and Shalah, Mira and Groscot, Raphael and Guibas, Leonidas J.},
title = {Composite Shape Modeling via Latent Space Factorization},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}