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.
@article{arxiv.1904.02199,
title = {3D-BEVIS: Bird's-Eye-View Instance Segmentation},
author = {Cathrin Elich and Francis Engelmann and Theodora Kontogianni and Bastian Leibe},
journal= {arXiv preprint arXiv:1904.02199},
year = {2019}
}