Given a single RGB image of a complex outdoor road scene in the perspective view, we address the novel problem of estimating an occlusion-reasoned semantic scene layout in the top-view. This challenging problem not only requires an accurate understanding of both the 3D geometry and the semantics of the visible scene, but also of occluded areas. We propose a convolutional neural network that learns to predict occluded portions of the scene layout by looking around foreground objects like cars or pedestrians. But instead of hallucinating RGB values, we show that directly predicting the semantics and depths in the occluded areas enables a better transformation into the top-view. We further show that this initial top-view representation can be significantly enhanced by learning priors and rules about typical road layouts from simulated or, if available, map data. Crucially, training our model does not require costly or subjective human annotations for occluded areas or the top-view, but rather uses readily available annotations for standard semantic segmentation in the perspective view. We extensively evaluate and analyze our approach on the KITTI and Cityscapes data sets.
|Title of host publication||Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings|
|Editors||Yair Weiss, Vittorio Ferrari, Cristian Sminchisescu, Martial Hebert|
|Number of pages||17|
|State||Published - 2018|
|Event||15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany|
Duration: Sep 8 2018 → Sep 14 2018
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||15th European Conference on Computer Vision, ECCV 2018|
|Period||9/8/18 → 9/14/18|
Bibliographical noteFunding Information:
This material is based upon work supported by the National Science Foundation under Grant No. (IIS-1553116). The work was part of M. Zhai’s internship at NEC Labs America, in Cupertino.
Acknowledgments. This material is based upon work supported by the National Science Foundation under Grant No. (IIS-1553116). The work was part of M. Zhai’s internship at NEC Labs America, in Cupertino.
© Springer Nature Switzerland AG 2018.
- 3D scene understanding
- Occlusion reasoning
- Semantic top-view representations
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science (all)