Nonparametric semantic segmentation for 3D street scenes
He, Hu & Upcroft, Ben (2013) Nonparametric semantic segmentation for 3D street scenes. In Amato, Nancy (Ed.) IROS2013: IEEE/RSJ International Conference on Intelligent Robots and Systems : New Horizon, 3-8 November 2013, Tokyo Big Sight, Tokyo, Japan.
In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.
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|Item Type:||Conference Paper|
|Keywords:||semantic segmentation, 3d reconstruction, nonparametric model|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 Please consult author(s)/creators|
|Deposited On:||19 Jul 2013 00:52|
|Last Modified:||20 Jan 2014 23:51|
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