ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 427-434, 2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
20 Aug 2015
M. Menze1, C. Heipke1, and A. Geiger2 1Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Hannover, Germany
2Perceiving Systems Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany
Keywords: Scene Flow, Motion Estimation, 3D Reconstruction, Active Shape Model, Object Detection Abstract. driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.
Conference paper (PDF, 7449 KB)

Citation: Menze, M., Heipke, C., and Geiger, A.: JOINT 3D ESTIMATION OF VEHICLES AND SCENE FLOW, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 427-434,, 2015.

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