Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval
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초록
This paper presents a novel convolutional neural network based multi-feature fusion learning method for nonrigid3D model retrieval, which can investigate the useful discriminative information of the heat kernelsignature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shapedistributions of the two kinds of descriptors to represent the 3D model and use them as the input to thenetworks. Then we construct two convolutional neural networks for the HKS distribution and the WKSdistribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only canexploit more discriminative characteristics of the two descriptors, but also can complement the correlatedinformation between the two kinds of descriptors. Furthermore, to further improve the performance of thedescription ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.
Abstract
This paper presents a novel convolutional neural network based multi-feature fusion learning method for nonrigid3D model retrieval, which can investigate the useful discriminative information of the heat kernelsignature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shapedistributions of the two kinds of descriptors to represent the 3D model and use them as the input to thenetworks. Then we construct two convolutional neural networks for the HKS distribution and the WKSdistribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only canexploit more discriminative characteristics of the two descriptors, but also can complement the correlatedinformation between the two kinds of descriptors. Furthermore, to further improve the performance of thedescription ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.
참고문헌 (26)
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