Effective 3-D Shape Retrieval by Integrating Traditional Descriptors and Pointwise Convolution

• Published in: IEEE transactions on multimedia Vol. 21; no. 12; pp. 3164 - 3177
• Main Authors: Kuang, Zhenzhong, Yu, Jun, Zhu, Suguo, Li, Zongmin, Fan, Jianping
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3-D Shape retrieval and recognition; Algorithms; Artificial neural networks; Convolution; Deep learning; Feature extraction; Feature recognition; intrinsic point descriptor; isometric variation; Knowledge management; Knowledge transfer; Object recognition; parallel knowledge transfer; Performance enhancement; pointwise convolution; Retrieval; Shape; Shape recognition; Three-dimensional displays
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2019.2918729

The applications of isometric 3-D objects have recently received sufficient attention and, thus, it is very attractive to retrieve such isometric 3-D objects from large-scale collections. Although existing approaches have presented some interesting ideas, their performance is limited to their ability on feature representation. To improve the performance of 3-D object (shape) recognition, some recent algorithms prefer using complicated deep neural networks to learn discriminative features, but they consume huge amounts of computing resources. Instead, this paper presents a more effective solution by seamlessly integrating the traditional local descriptor with a deep pointwise convolutional network to extract 1-D features for shape recognition and retrieval. To reduce the costs of designing a complicated deep network, the first step of our algorithm is to describe the shape deformation by sampling a set of intrinsic point descriptors. Then, we introduce a simple yet effective pointwise convolutional network to integrate these descriptors as a global feature and the learning process can be significantly accelerated with the help of downsampling. Furthermore, a knowledge transfer strategy is used to upgrade our feature by compensating for information loss. Finally, we carry out experimental evaluations over popular shape benchmarks, and the results suggest that our approach exhibits superior accuracy rates and robustness on shape recognition and retrieval.