The Shape Part Slot Machine: Contact-Based Reasoning for Generating 3D Shapes from Parts
Abstract
We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning. Our method represents each shape as a graph of ""slots,"" where each slot is a region of contact between two shape parts. Based on this representation, we design a graph-neural-network-based model for generating new slot graphs and retrieving compatible parts, as well as a gradient-descent-based optimization scheme for assembling the retrieved parts into a complete shape that respects the generated slot graph. This approach does not require any semantic part labels; interestingly, it also does not require complete part geometries---reasoning about the regions where parts connect proves sufficient to generate novel, high-quality 3D shapes. We demonstrate that our method generates shapes that outperform existing modeling-by-assembly approaches in terms of quality, diversity, and structural complexity.
Cite
Text
Wang et al. "The Shape Part Slot Machine: Contact-Based Reasoning for Generating 3D Shapes from Parts." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-20062-5_35Markdown
[Wang et al. "The Shape Part Slot Machine: Contact-Based Reasoning for Generating 3D Shapes from Parts." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/wang2022eccv-shape/) doi:10.1007/978-3-031-20062-5_35BibTeX
@inproceedings{wang2022eccv-shape,
title = {{The Shape Part Slot Machine: Contact-Based Reasoning for Generating 3D Shapes from Parts}},
author = {Wang, Kai and Guerrero, Paul and Kim, Vladimir G. and Chaudhuri, Siddhartha and Sung, Minhyuk and Ritchie, Daniel},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
doi = {10.1007/978-3-031-20062-5_35},
url = {https://mlanthology.org/eccv/2022/wang2022eccv-shape/}
}