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- W2606813328 abstract "This study questions why existing local shape descriptors have high dimensionalities (up to hundreds) despite simplicity of local shapes. We derived an answer from a historical context and provided an alternative solution by proposing a new compact descriptor. Although existing descriptors can express complicated shapes and depth sensors have been improved, complex shapes are rarely observed in an ordinary environment and a depth sensor only captures a single side of a surface with noise. Therefore, we designed a new descriptor based on principal curvatures, which is compact but practically useful. For verification, the CoRBS dataset, the RGB-D Scenes dataset and the RGB-D Object dataset were used to compare the proposed descriptor with existing descriptors in terms of shape, instance, and category recognition rate. The proposed descriptor showed a comparable performance with existing descriptors despite its low dimensionality of 4." @default.
- W2606813328 created "2017-04-28" @default.
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- W2606813328 date "2017-04-16" @default.
- W2606813328 modified "2023-09-26" @default.
- W2606813328 title "New Compact 3-Dimensional Shape Descriptor for a Depth Camera in Indoor Environments" @default.
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- W2606813328 doi "https://doi.org/10.3390/s17040876" @default.
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