Unsupervised Dense Deformation Embedding Network for Template-Free Shape Correspondence
文献类型：会议论文
| 作者 | Chen RH(陈荣瀚)1,2,3; Cong Y(丛杨)1,2 ; Dong JH(董家华)
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| 出版日期 | 2021 |
| 会议日期 | October 10-17, 2021 |
| 会议地点 | Montreal, Canada |
| 页码 | 8341-8350 |
| 英文摘要 | Shape correspondence from 3D deformation learning has attracted appealing academy interests recently. Nevertheless, current deep learning based methods require the supervision of dense annotations to learn per-point translations, which severely over-parameterize the deformation process. Moreover, they fail to capture local geometric details of original shape via global feature embedding. To address these challenges, we develop a new Unsupervised Dense Deformation Embedding Network (i.e., UD2E-Net), which learns to predict deformations between non-rigid shapes from dense local features. Since it is non-trivial to match deformation-variant local features for deformation prediction, we develop an Extrinsic-Intrinsic Autoencoder to first encode extrinsic geometric features from source into intrinsic coordinates in a shared canonical shape, with which the decoder then synthesizes corresponding target features. Moreover, a bounded maximum mean discrepancy loss is developed to mitigate the distribution divergence between the synthesized and original features. To learn natural deformation without dense supervision, we introduce a coarse parameterized deformation graph, for which a novel trace and propagation algorithm is proposed to improve both the quality and efficiency of the deformation. Our UD2E-Net outperforms state-of-the-art unsupervised methods by 24% on Faust Inter challenge and even supervised methods by 13% on Faust Intra challenge. |
| 产权排序 | 1 |
| 会议录 | 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 1550-5499 |
| ISBN号 | 978-1-6654-2812-5 |
| 源URL | [http://ir.sia.cn/handle/173321/29963]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Cong Y(丛杨) |
| 作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences 3.University of Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Chen RH,Cong Y,Dong JH. Unsupervised Dense Deformation Embedding Network for Template-Free Shape Correspondence[C]. 见:. Montreal, Canada. October 10-17, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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