Composite Shape Modeling via Latent Space Factorization
| Autor: | Raphael Groscot, Leonidas J. Guibas, Panos Achlioptas, Anastasia Dubrovina, Mira Shalah, Fei Xia |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 020207 software engineering 02 engineering and technology Iterative reconstruction Solid modeling 010501 environmental sciences 01 natural sciences Factorization 0202 electrical engineering electronic engineering information engineering Embedding Artificial intelligence business Algorithm 0105 earth and related environmental sciences |
| Zdroj: | ICCV |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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