M.Sc. Jonas Schult
Room 122
Email: schult (at) vision.rwth-aachen.de


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

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}

Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds

Francis Engelmann, Theodora Kontogianni, Jonas Schult, Bastian Leibe
IEEE European Conference on Computer Vision (ECCV'18), GMDL Workshop

In this paper, we present a deep learning architecture which addresses the problem of 3D semantic segmentation of unstructured point clouds. Compared to previous work, we introduce grouping techniques which define point neighborhoods in the initial world space and the learned feature space. Neighborhoods are important as they allow to compute local or global point features depending on the spatial extend of the neighborhood. Additionally, we incorporate dedicated loss functions to further structure the learned point feature space: the pairwise distance loss and the centroid loss. We show how to apply these mechanisms to the task of 3D semantic segmentation of point clouds and report state-of-the-art performance on indoor and outdoor datasets.

» Show BibTeX

author = {Francis Engelmann and
Theodora Kontogianni and
Jonas Schult and
Bastian Leibe},
title = {Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds},
booktitle = {{IEEE} European Conference on Computer Vision, GMDL Workshop, {ECCV}},
year = {2018}

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