Abstract
Depth perception from single monocular images is a challenging problem in computer vision. Since the single image is lack of features of context, we only find all the cues from the local image. This paper presents a novel method for 3D depth perception from a single monocular image containing the ground to estimate the absolute depthmaps more accurately. Different from previous methods, in our method, we first generates the ground plane depth coordinate system from a single monocular image by image-forming principle, and then locates the objects in image with the coordinate system using the geometric characteristics. At last, we provide an method to estimate the accurate depthmaps. The experiments show that our method outperforms the state-of-the-art single-image depth perception methods both in relative depth perception and absolute depth perception.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Batra, D., Saxena, A.: Learning the right model: Efficient max-margin learning in laplacian crfs. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2136–2143 (2012)
Calderero, F., Caselles, V.: Recovering relative depth from low-level features without explicit t-junction detection and interpretation. International Journal of Computer Vision 104(1), 38–68 (2013)
Cherian, A., Morellas, V., Papanikolopoulos, N.: Accurate 3d ground plane estimation from a single image. In: 2009 IEEE International Conference on Robotics and Automation (ICRA), pp. 2243–2249. IEEE (2009)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)
Hedau, V., Hoiem, D., Forsyth, D.: Recovering free space of indoor scenes from a single image. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2807–2814. IEEE (2012)
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: Rgb-d mapping: Using kinect-style depth cameras for dense 3d modeling of indoor environments. The International Journal of Robotics Research 31(5), 647–663 (2012)
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: Rgb-d mapping: Using depth cameras for dense 3d modeling of indoor environments. In: Experimental Robotics, pp. 477–491. Springer (2014)
Hoiem, D., Adviser-Efros, A.A., Adviser-Hebert, M.: Seeing the world behind the image: spatial layout for three-dimensional scene understanding. Carnegie Mellon University (2007)
Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. In: 2005 10th IEEE International Conference on Computer Vision, pp. 654–661. IEEE (2005)
Hoiem, D., Efros, A.A., Hebert, M.: Recovering surface layout from an image. International Journal of Computer Vision 75(1), 151–172 (2007)
Hoiem, D., Efros, A.A., Hebert, M.: Closing the loop in scene interpretation. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)
Hoiem, D., Efros, A.A., Hebert, M.: Recovering occlusion boundaries from an image. International Journal of Computer Vision 91(3), 328–346 (2011)
Kratz, L., Nishino, K.: Factorizing scene albedo and depth from a single foggy image. In: 2009 12th IEEE International Conference on Computer Vision, pp. 1701–1708. IEEE (2009)
Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics (TOG) 26(3), 70 (2007)
Lin, J., Ji, X., Xu, W., Dai, Q.: Absolute depth estimation from a single defocused image. IEEE Transactions on Image Processing: a publication of the IEEE Signal Processing Society 22(11), 4545 (2013)
Namboodiri, V.P., Chaudhuri, S.: Recovery of relative depth from a single observation using an uncalibrated (real-aperture) camera. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–6. IEEE (2008)
Nicolas, H.: Depth analysis for surveillance videos in the h. 264 compressed domain. In: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp. 146–149. IEEE (2012)
Saxena, A.: Monocular depth perception and robotic grasping of novel objects. Ph.D. thesis, Citeseer (2009)
Saxena, A., Chung, S.H., Ng, A.Y.: Learning depth from single monocular images. In: Neural Information Processing Systems Conference (NIPS), vol. 18, pp. 1–8 (2005)
Saxena, A., Chung, S.H., Ng, A.Y.: 3-d depth reconstruction from a single still image. International Journal of Computer Vision 76(1), 53–69 (2008)
Saxena, A., Schulte, J., Ng, A.Y.: Depth estimation using monocular and stereo cues. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 2197–2203. Morgan Kaufmann Publishers Inc. (2007)
Saxena, A., Sun, M., Ng, A.Y.: Make3d: Depth perception from a single still image. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 1571–1576 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, H., Li, K., Lv, F., Pei, J. (2015). 3D Depth Perception from Single Monocular Images. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-14445-0_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14444-3
Online ISBN: 978-3-319-14445-0
eBook Packages: Computer ScienceComputer Science (R0)