Profile

M.Sc. Theodora Kontogianni
Room 122
Phone: +49 241 80 20 759
Email: kontogianni@vision.rwth-aachen.de


Publications


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

@inproceedings{3dsemseg_ECCVW18,
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}
}






Theodora Kontogianni, Francis Engelmann, Alexander Hermans, Bastian Leibe
IEEE International Conference on Computer Vision (ICCV'17) 3DRMS Workshop

Deep learning approaches have made tremendous progress in the field of semantic segmentation over the past few years. However, most current approaches operate in the 2D image space. Direct semantic segmentation of unstructured 3D point clouds is still an open research problem. The recently proposed PointNet architecture presents an interesting step ahead in that it can operate on unstructured point clouds, achieving decent segmentation results. However, it subdivides the input points into a grid of blocks and processes each such block individually. In this paper, we investigate the question how such an architecture can be extended to incorporate larger-scale spatial context. We build upon PointNet and propose two extensions that enlarge the receptive field over the 3D scene. We evaluate the proposed strategies on challenging indoor and outdoor datasets and show improved results in both scenarios.

» Show BibTeX

@inproceedings{3dsemseg_ICCVW17,
author = {Francis Engelmann and
Theodora Kontogianni and
Alexander Hermans and
Bastian Leibe},
title = {Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds},
booktitle = {{IEEE} International Conference on Computer Vision, 3DRMS Workshop, {ICCV}},
year = {2017}
}






Theodora Kontogianni, Markus Mathias, Bastian Leibe
IEEE Conference on Computer Vision and Pattern Recognition (CVPR'16)

In this paper, we address the problem of object discovery in time-varying, large-scale image collections. A core part of our approach is a novel Limited Horizon Minimum Spanning Tree (LH-MST) structure that closely approximates the Minimum Spanning Tree at a small fraction of the latter’s computational cost. Our proposed tree structure can be created in a local neighborhood of the matching graph during image retrieval and can be efficiently updated whenever the image database is extended. We show how the LH-MST can be used within both single-link hierarchical agglomerative clustering and the Iconoid Shift framework for object discovery in image collections, resulting in significant efficiency gains and making both approaches capable of incremental clustering with online updates. We evaluate our approach on a dataset of 500k images from the city of Paris and compare its results to the batch version of both clustering algorithms.




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