Progressive shape-distribution-encoder for 3D shape retrieval

Xie, Jin, Zhu, Fan, Dai, Guoxian, Shao, Ling and Fang, Yi (2017) Progressive shape-distribution-encoder for 3D shape retrieval. IEEE Transactions on Image Processing, 26 (3). pp. 1231-1242. ISSN 1057-7149

[thumbnail of Accepted manuscript]
PDF (Accepted manuscript) - Accepted Version
Download (2MB) | Preview


Since there are complex geometric variations with 3D shapes, extracting efficient 3D shape features is one of the most challenging tasks in shape matching and retrieval. In this paper, we propose a deep shape descriptor by learning shape distributions at different diffusion time via a progressive shape-distribution-encoder (PSDE). First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometry structures of 3D shapes. Then, we propose to learn a deep shape feature through an unsupervised PSDE. Specially, the unsupervised PSDE aims at modeling the complex non-linear transform of the estimated shape distributions between consecutive diffusion time. In order to characterize the intrinsic structures of 3D shapes more efficiently, we stack multiple PSDEs to form a network structure. Finally, we concatenate all neurons in the middle hidden layers of the unsupervised PSDE network to form an unsupervised shape descriptor for retrieval. Furthermore, by imposing an additional constraint on the outputs of all hidden layers, we propose a supervised PSDE to form a supervised shape descriptor, where for each hidden layer the similarity between a pair of outputs from the same class is as small as possible and the similarity between a pair of outputs from different classes is as large as possible. The proposed method is evaluated on three benchmark 3D shape datasets with large geometric variations, i.e., McGill, SHREC’10 ShapeGoogle and SHREC’14 Human datasets, and the experimental results demonstrate the superiority of the proposed method to the existing approaches.

Item Type: Article
Uncontrolled Keywords: heat diffusion,3d shape retrieval,shape descriptor,denoising auto-encoder,heat kernel signature
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 18 Jan 2017 00:02
Last Modified: 22 Oct 2022 02:08
DOI: 10.1109/TIP.2017.2651408

Actions (login required)

View Item View Item