Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing
Generative 3D part assembly involves understanding part relationships and predicting their 6-DoF poses for assembling a realistic 3D shape. Prior work often focus on the geometry of individual parts, neglecting part-whole hierarchies of objects. Leveraging two key observations: 1) super-part poses p...
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Published in | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 20850 - 20859 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
16.06.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Generative 3D part assembly involves understanding part relationships and predicting their 6-DoF poses for assembling a realistic 3D shape. Prior work often focus on the geometry of individual parts, neglecting part-whole hierarchies of objects. Leveraging two key observations: 1) super-part poses provide strong hints about part poses, and 2) predicting super-part poses is easier due to fewer super-parts, we propose a part-whole-hierarchy message passing network for efficient 3D part assembly. We first introduce super-parts by grouping geometrically similar parts without any semantic labels. Then we employ a part-whole hierarchical encoder, wherein a super-part encoder predicts latent super-part poses based on input parts. Subsequently, we transform the point cloud using the latent poses, feeding it to the part encoder for aggregating super-part information and reasoning about part relationships to predict all part poses. In training, only ground-truth part poses are required. During inference, the predicted latent poses of super-parts enhance interpretability. Experimental results on the PartNet dataset show that our method achieves state-of-the-art performance in part and connectivity accuracy and enables an interpretable hierarchical part assembly. Code is available at https://github.com//pkudba/3DHPA. |
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ISSN: | 2575-7075 |
DOI: | 10.1109/CVPR52733.2024.01970 |