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- W2895860880 abstract "With the increasing popularity of 3D models, retrieving deformable 3D objects is becoming a crucial task. The state-of-the-art methods use complex deep neural networks to address this problem, which require lots of computational resources. In this paper, we develop a more effective solution by using point convolution. Our algorithm takes local point descriptors as the input and produces a global vector for shape retrieval. To save the efforts of designing complex deep convolutional neural network (CNN), we first use intrinsic point descriptors to describe the shape deformations. Then, a simple but effective point CNN network is developed to integrate the local shape information by performing subspace compression and fusion, which depends on an end-to-end learning process to link the local and global information for discriminative shape representation. The experimental results on popular benchmarks have verified that our algorithm is able to outperform the state-of-the-art methods." @default.
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- W2895860880 date "2018-07-01" @default.
- W2895860880 modified "2023-10-16" @default.
- W2895860880 title "Deep Point Convolutional Approach for 3D Model Retrieval" @default.
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- W2895860880 doi "https://doi.org/10.1109/icme.2018.8486479" @default.
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