6533b7d5fe1ef96bd1263cfb
RESEARCH PRODUCT
Incomplete 3D motion trajectory segmentation and 2D-to-3D label transfer for dynamic scene analysis
Cédric DemonceauxDanda Pani PaudelYohan FougerolleCansen JiangDavid Fofisubject
Computer scienceScene UnderstandingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Motion (physics)[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]0502 economics and business0202 electrical engineering electronic engineering information engineering[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Computer visionSegmentationMotion Segmentation050210 logistics & transportationbusiness.industry[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO][ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]05 social sciences3D reconstruction[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]2D to 3D conversionFeature (computer vision)TrajectoryKey (cryptography)Robot020201 artificial intelligence & image processingArtificial intelligence3D Reconstructionbusinessdescription
International audience; 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.
year | journal | country | edition | language |
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2017-09-24 | 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |