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Incomplete 3D Motion Trajectory Segmentation and 2D-to-3D Label Transfer for Dynamic Scene Analysis

Cansen Jiang, Danda Paudel, Yohan Fougerolle, David Fofi, Cédric Demonceaux
Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on
2017

Abstract

The knowledge of the static scene parts and the moving objects in a dynamic scene plays a vital role for scene modelling, understanding, and landmark-based robot navigation. The key information for these tasks lies on semantic labels of the scene parts and the motion trajectories of the dynamic objects. In this work, we propose a method that segments the 3D feature trajectories based on their motion behaviours, and assigns them semantic labels using 2D-to-3D label transfer. These feature trajectories are constructed by using the proposed trajectory recovery algorithm which takes the loss of feature tracking into account. We introduce a complete framework for static-map and dynamic objects' reconstruction, as well as semantic scene understanding for a calibrated and moving 2D-3D camera setup. Our motion segmentation approach is faster by two orders of magnitude, while performing better than the state-of-the-art 3D motion segmentation methods, and successfully handles the previously discarded incomplete trajectory scenarios.


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@InProceedings{eth_biwi_01423,
  author = {Cansen Jiang and Danda Paudel and Yohan Fougerolle and David Fofi and Cédric Demonceaux},
  title = {Incomplete 3D Motion Trajectory Segmentation and 2D-to-3D Label Transfer for Dynamic Scene Analysis},
  booktitle = {Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on},
  year = {2017},
  keywords = {}
}