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arXiv · 1904.02199

3D-BEVIS: Bird's-Eye-View Instance Segmentation

Cathrin Elich·Francis Engelmann·Theodora Kontogianni·Bastian Leibe
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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.

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Cathrin Elich, Francis Engelmann, Theodora Kontogianni, Bastian Leibe. 2019-04-03. 3D-BEVIS: Bird's-Eye-View Instance Segmentation. https://doi.org/10.1007/978-3-030-33676-9_4

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