An algorithm is proposed to estimate the 3D ground plane region and scene depth information from a monocular image captured by a vehicle-borne camera. Firstly, information about image defocus, image saturation and dark channel prior are fused to estimate a relative depth map of the scene. Then, the 3D ground plane can be inferred by using a bilateral median filter based on the assumption that the horizon is piecewise smooth. Finally, absolute depth map can be obtained by using the principle of imaging geometry. To verify the effectiveness of the proposed algorithm, not only numerous comparative experiments are performed on an offline computer, but also it is applied to the outdoor autonomous obstacle avoidance of a robot vehicle. Experiment results demonstrate that both the 3D ground plane and scene depth information can be well estimated by the proposed algorithm, with which the robot vehicle can successfully detect and avoid the obstacles.
 Zhang R, Tsai P, Cryer J E, et al. Shape from shading: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(8): 690-706.  Chaudhuri S, Rajagopalan A N. Depth from defocus: A real aperture imaging approach[M]. Berlin, Germany: Springer-Verlag, 1999. Loh A M, Kovesi P. Estimation of surface normal of a curved surface using texture[C]//Proceedings of the Digital Image Computing: Techniques and Applications Conference. Piscataway, USA: IEEE, 2003. Delage E, Lee H, Ng A Y. A dynamic Bayesian network model for autonomous 3D reconstruction from a single indoor image[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA: IEEE, 2006: 2418-2428. Sun M, Saxena A, Ng A Y. Learning 3-D scene structure from a single still image[C]//11th Conference on Computer Vision. Piscataway, USA: IEEE, 2007: 1-8. Saxena A, Chung S H, Ng A Y. 3-D depth reconstruction from a single still image[J]. International Journal of Computer Vision, 2008, 76(1): 53-69. Saxena A, Chung S H, Ng A. Learning depth from single monocular images[C]//Advances in Neural Information Processing Systems. 2005: 1161-1168. Hoiem D, Efros A A, Hebert M. Automatic photo pop-up[J]. ACM Transactions on Graphics, 2005, 24(3): 577-584.  Huttenlocher D, Felzenszalb F. Efficient graph based image segmentation[J]. International Journal of Computer Vision, 2004, 59(2): 167-181.  Cherian A, Morellas V, Papanikolopoulos N. Accurate 3D ground plane estimation from a single image[C]//International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2009: 2243-2249. Saxena A, Sun M, Ng A Y. Make3D: Learning 3D scene structure from a single still image[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(5): 824-840.  Hu H, Haan G. Adaptive image restoration based on local robust blur estimation[C]//9th International Conference on Advanced Concepts for Intelligent Vision Systems. Berlin, Germany: Springer-Verlag, 2007: 461-472. He K, Sun J, Tang X. Single image haze removal using dark channel prior[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA: IEEE, 2009: 1956-1963. Hecht E. Optics[M]. 4th ed. Hoboken, USA: John Wiley & Sons Inc., 2001. Levin A, Lischinski D, Weiss Y. A closed form solution to natural image matting[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA: IEEE, 2006: 61-68. He K, Sun J, Tang X. Guided image filtering[C]//11th European Conference on Computer Vision. Berlin, Germany: Springer-Verlag, 2010: 1-14. Tarel J P, Hautiere N. Fast visibility restoration from a single color or gray level image[C]//11th Conference on Computer Vision. Piscataway, USA: IEEE, 2009: 2201-2208. Zhang Z Y. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.  Make3D: Convert your still image into 3D model[EB/OL].[2013-01-14]. http://make3d.cs.cornell.edu/.