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Understanding High-Level Semantics by Modeling Traffic Patterns
In this paper, we are interested in understanding the semantics of outdoor scenes in the context of autonomous driving. Towards this goal, we propose a generative model of 3D urban scenes which is able to reason not only about the geometry and objects present in the scene, but also about the high-level semantics in the form of traffic patterns. We found that a small number of patterns is sufficient to model the vast majority of traffic scenes and show how these patterns can be learned. As evidenced by our experiments, this high-level reasoning significantly improves the overall scene estimation as well as the vehicle-to-lane association when compared to state-of-the-art approaches. All data and code will be made available upon publication.
@inproceedings{Zhang2013ICCV, title = {Understanding High-Level Semantics by Modeling Traffic Patterns}, booktitle = {International Conference on Computer Vision}, abstract = {In this paper, we are interested in understanding the semantics of outdoor scenes in the context of autonomous driving. Towards this goal, we propose a generative model of 3D urban scenes which is able to reason not only about the geometry and objects present in the scene, but also about the high-level semantics in the form of traffic patterns. We found that a small number of patterns is sufficient to model the vast majority of traffic scenes and show how these patterns can be learned. As evidenced by our experiments, this high-level reasoning significantly improves the overall scene estimation as well as the vehicle-to-lane association when compared to state-of-the-art approaches. All data and code will be made available upon publication.}, pages = {3056-3063}, address = {Sydney, Australia}, month = dec, year = {2013}, slug = {zhang2013iccv}, author = {Zhang, Hongyi and Geiger, Andreas and Urtasun, Raquel}, month_numeric = {12} }