Object scene flow for autonomous vehicles

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 param...

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Vydáno v:2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) s. 3061 - 3070
Hlavní autoři: Menze, Moritz, Geiger, Andreas
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.06.2015
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ISSN:1063-6919, 1063-6919
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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.
AbstractList 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.
Author Menze, Moritz
Geiger, Andreas
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  givenname: Andreas
  surname: Geiger
  fullname: Geiger, Andreas
  email: andreas.geiger@tue.mpg.de
  organization: MPI Tubingen, Tubingen, Germany
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Snippet 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...
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StartPage 3061
SubjectTerms Autonomous
Benchmark testing
Computer vision
Decomposition
Design automation
Dynamics
Optical imaging
Optical sensors
Pattern recognition
Solid modeling
Three dimensional
Three dimensional models
Three-dimensional displays
Vehicle dynamics
Vehicles
Title Object scene flow for autonomous vehicles
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