Full and partial shape similarity through sparse descriptor reconstruction

Wan, Lei; Zhang, Changqing; Zhang, Hao

doi:10.1007/s00371-016-1293-1

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…The shape descriptor is an abstract and concise representation of the 3D shape, which reflects the geometric feature information of the mesh. The shape descriptor has the characteristics of invariance of mesh rotation and translation and insensitivity of mesh deformation [33]. The input vector contains a variety of shape descriptors, not only increasing the complexity of OS-ELM but also increasing the computational complexity of the algorithm, which cannot satisfy the requirement of low computational complexity in the Web environment.…”

Section: Online Rapid Mesh Segmentation Based On the Os-elm A Pcamentioning

confidence: 99%

An Online Rapid Mesh Segmentation Method Based on an Online Sequential Extreme Learning Machine

et al. 2019

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The existing mesh segmentation methods currently require long training times and have high computational complexity. Consequently, many of these methods cannot meet the rapid requirements of digital geometry processing in the Web environment. This paper proposes an online rapid mesh segmentation method based on an online sequential extreme learning machine (OS-ELM). In the training stage, the OS-ELM is trained by analyzing the mapping relationship between the shape descriptors of the mesh and the Gaussian curvature threshold. We reduce the dimensionality of the shape descriptor vector via principal component analysis (PCA) and extract the Gaussian curvature threshold of the mesh as the sample label using statistics. In the segmentation stage, the Gaussian curvature threshold is quickly extracted to realize the online rapid mesh segmentation via the OS-ELM. Simultaneously, the OS-ELM is updated to realize online incremental learning based on a small number of training samples. Our method is verified using the meshes provided from ShapeNetCore. The experimental results indicate that segmentation results similar to manual segmentation can be rapidly generated online using our method. INDEX TERMS Online sequential extreme learning machine, rapid mesh segmentation, incremental learning, Gaussian curvature threshold, web environment.

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Section: Online Rapid Mesh Segmentation Based On the Os-elm A Pcamentioning

confidence: 99%