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• W2928059219 abstract "Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of object classification and semantic segmentation. However, the task of instance segmentation is less explored. In this work, we present 3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on point clouds. Following the idea of previous proposal-free instance segmentation approaches, our model learns a feature embedding and groups the obtained feature space into semantic instances. Current point-based methods scale linearly with the number of points by processing local sub-parts of a scene individually. However, to perform instance segmentation by clustering, globally consistent features are required. Therefore, we propose to combine local point geometry with global context information from an intermediate bird's-eye view representation." @default.
• W2928059219 created "2019-04-11" @default.
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• W2928059219 date "2019-01-01" @default.
• W2928059219 modified "2023-10-03" @default.
• W2928059219 title "3D Bird’s-Eye-View Instance Segmentation" @default.
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• W2928059219 doi "https://doi.org/10.1007/978-3-030-33676-9_4" @default.
• W2928059219 hasPublicationYear "2019" @default.
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