New Compact 3-Dimensional Shape Descriptor for a Depth Camera in Indoor Environments
AbstractThis 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. View Full-Text
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Choi, H.; Kim, E. New Compact 3-Dimensional Shape Descriptor for a Depth Camera in Indoor Environments. Sensors 2017, 17, 876.
Choi H, Kim E. New Compact 3-Dimensional Shape Descriptor for a Depth Camera in Indoor Environments. Sensors. 2017; 17(4):876.Chicago/Turabian Style
Choi, Hyukdoo; Kim, Euntai. 2017. "New Compact 3-Dimensional Shape Descriptor for a Depth Camera in Indoor Environments." Sensors 17, no. 4: 876.
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