3D-BEVIS: Birds-Eye-View Instance Segmentation

Cathrin Elich, Francis Engelmann, Jonas Schult, Theodora Kontogianni, Bastian Leibe
Technical Report

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.

» Show BibTeX

@article{Elich19CoRR,
author = {Elich, Cathrin and Engelmann, Francis and Schult, Jonas and Kontogianni, Theodora and Leibe, Bastian},
title = {{3D-BEVIS: Birds-Eye-View Instance Segmentation}},
journal = {CoRR},
volume = {abs/1904.02199},
year = {2019}
}




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