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- W2898888235 abstract "This paper presents an effective 3D local feature descriptor, which is called Gradient Direction Accumulation-based Heat Kernel Signature (GDA-HKS) descriptor, and its application in nonrigid 3D model retrieval. The GDA-HKS descriptor is based on the heat kernel signature, and it is scale invariant and robust to the nonrigid deformation of the 3D model. Compared with the SI-HKS descriptor, the GDA-HKS descriptor is constructed directly in the time domain, and it can effectively avoid the loss of high frequency information. The absolute gradient difference is used to encode the GDA-HKS descriptor, which can describe the changing trend of the one-dimensional signal more effectively. Extensive experimental results have validated the effectiveness of the designed GDA-HKS descriptor." @default.
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- W2898888235 date "2019-06-07" @default.
- W2898888235 modified "2023-09-27" @default.
- W2898888235 title "Gradient Direction Accumulation-based Heat Kernel Signature Descriptor for Nonrigid 3D Model Retrieval" @default.
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- W2898888235 doi "https://doi.org/10.1142/s0218001419550085" @default.
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