SCORES
| Autor: | Siddhartha Chaudhuri, Hao Zhang, Chenyang Zhu, Kai Xu, Renjiao Yi |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 020207 software engineering Pattern recognition 02 engineering and technology Computer Graphics and Computer-Aided Design Autoencoder Graphics (cs.GR) Machine Learning (cs.LG) Range (mathematics) Computer Science - Graphics Recurrent neural network Prior probability 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Code (cryptography) Substructure 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ACM Transactions on Graphics. 37:1-14 |
| ISSN: | 1557-7368 0730-0301 |
| DOI: | 10.1145/3272127.3275008 |
| Popis: | We introduce SCORES, a recursive neural network for shape composition. Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of the parts. It outputs an optimized part structure for the composed shape, leading to high-quality geometry construction. A unique feature of our composition network is that it is not merely learning how to connect parts. Our goal is to produce a coherent and plausible 3D shape, despite large incompatibilities among the input parts. The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss. We design SCORES as a recursive autoencoder network. During encoding, the input parts are recursively grouped to generate a root code. During synthesis, the root code is decoded, recursively, to produce a new, coherent part assembly. Assembled shape structures may be novel, with little global resemblance to training exemplars, yet have plausible substructures. SCORES therefore learns a hierarchical substructure shape prior based on per-node losses. It is trained on structured shapes from ShapeNet, and is applied iteratively to reduce the plausibility loss.We showresults of shape composition from multiple sources over different categories of man-made shapes and compare with state-of-the-art alternatives, demonstrating that our network can significantly expand the range of composable shapes for assembly-based modeling. Accepted to SIGGRAPH Asia 2018. Corresponding Author: Kai Xu (kevin.kai.xu@gmail.com) |
| Databáze: | OpenAIRE |
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