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Content Provider | IEEE Xplore Digital Library |
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Author | Menze, M. Geiger, A. |
Copyright Year | 2015 |
Description | Author affiliation: MPI Tubingen, Tubingen, Germany (Geiger, A.) || Leibniz Univ. Hannover, Hannover, Germany (Menze, M.) |
Abstract | This paper proposes 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. |
Starting Page | 3061 |
Ending Page | 3070 |
File Size | 2924077 |
Page Count | 10 |
File Format | |
ISSN | 10636919 |
e-ISBN | 9781467369640 |
DOI | 10.1109/CVPR.2015.7298925 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-06-07 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Three-dimensional displays Solid modeling Benchmark testing Design automation Optical imaging Optical sensors Vehicle dynamics |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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