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Logo: Institut für Photogrammetrie und Geoinformatik/Leibniz Universität Hannover
Logo Leibniz Universität Hannover
Logo: Institut für Photogrammetrie und Geoinformatik/Leibniz Universität Hannover
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Object Scene Flow

Bearbeitung:M. Menze
Laufzeit:since 01.03.2014
Link:www.cvlibs.net/projects/objectsceneflow

Scene flow results on the proposed dataset. Top: Estimated moving objects (in blue and green) with the background object in transparent. Centre: Resulting optical flow map. Bottom: Optical flow ground truth.In this project, we propose a novel model and dataset for 3D scene flow estimation with an application to autonomous driving. Taking advantage of the fact that outdoor scenes often decompose into a small number of independently moving objects, we represent each element in the scene by its rigid motion parameters and each superpixel by a 3D plane as well as an index to the corresponding object. This minimal representation increases robustness and leads to a discrete-continuous CRF where the data term decomposes into pairwise potentials between superpixels and objects. Moreover, our model intrinsically segments the scene into its constituting dynamic components. We demonstrate the performance of our model on existing benchmarks as well as a novel realistic dataset with scene flow ground truth. We obtain this dataset by annotating 400 dynamic scenes from the KITTI raw data collection using detailed 3D CAD models for all vehicles in motion. Our experiments also reveal novel challenges which cannot be handled by existing methods.

 

 

Publikationen

Menze, M; Geiger, A. (2015): Object Scene Flow for Autonomous Vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, June 2015, pp. 3061-3070 more

Menze, M.; Heipke, C.; Geiger, A. (2015): Joint 3D Estimation of Vehicles and Scene Flow. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3/W5, pp. 427-434, 2015  | file |

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