多流变形中的形状嵌入和检索

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Computational Visual Media Pub Date : 2024-02-08 DOI:10.1007/s41095-022-0315-3
Baiqiang Leng, Jingwei Huang, Guanlin Shen, Bin Wang
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引用次数: 0

摘要

我们提出了一个统一的三维流框架,用于联合学习不同类别的形状嵌入和变形。我们的目标是从不完美的点云中恢复形状,方法是在变形后在形状库中拟合最佳形状模板。因此,我们学习了用于模板检索的形状嵌入和用于稳健变形的基于流的网络。我们注意到,不同形状类别的变形流可能大不相同。因此,我们引入了一个新颖的多集线器模块来学习多种变形模式,以纳入这种变化,从而提供一个可处理不同类别的各种物体的网络。形状嵌入的设计目的是在潜在空间中检索作为最近邻的最合适模板。我们用嵌入中的微小结构取代了标准的全连接层,从而大大降低了网络的复杂性,并进一步提高了变形质量。通过定性和定量比较,实验表明我们的方法优于现有的最先进方法。最后,我们的方法提供了高效灵活的变形,可进一步用于新颖的形状设计。
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Shape embedding and retrieval in multi-flow deformation

We propose a unified 3D flow framework for joint learning of shape embedding and deformation for different categories. Our goal is to recover shapes from imperfect point clouds by fitting the best shape template in a shape repository after deformation. Accordingly, we learn a shape embedding for template retrieval and a flow-based network for robust deformation. We note that the deformation flow can be quite different for different shape categories. Therefore, we introduce a novel multi-hub module to learn multiple modes of deformation to incorporate such variation, providing a network which can handle a wide range of objects from different categories. The shape embedding is designed to retrieve the best-fit template as the nearest neighbor in a latent space. We replace the standard fully connected layer with a tiny structure in the embedding that significantly reduces network complexity and further improves deformation quality. Experiments show the superiority of our method to existing state-of-the-art methods via qualitative and quantitative comparisons. Finally, our method provides efficient and flexible deformation that can further be used for novel shape design.

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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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