Deep convolutional neural fields for depth estimation from a single image

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 5162-5170

  Liu, Fayao ^a,b   Shen, Chunhua ^a,b   Lin, Guosheng ^a,b  

^a UNIVERSITY OF ADELAIDE   (Australia)

^b AUSTRALIAN CENTRE FOR ROBOTIC VISION   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; ESTIMATION; NEURAL NETWORKS; PATTERN RECOGNITION; RANDOM PROCESSES; STEREO IMAGE PROCESSING;

CLOSED FORM SOLUTIONS; CONDITIONAL RANDOM FIELD; CONVOLUTIONAL NEURAL NETWORK; NEURAL FIELD MODELS; SOURCES OF INFORMATIONS; STEREO CORRESPONDENCES; STRUCTURED LEARNING; VISION APPLICATIONS;

PROBLEM SOLVING;

EID: 84959229072     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299152     Document Type: Conference Paper

References (24)

1. D. Eigen, C. Puhrsch, and R. Fergus, "Depth map prediction from a single image using a multi-scale deep network," in Proc. Adv. Neural Inf. Process. Syst., 2014.
2. V. Hedau, D. Hoiem, and D. A. Forsyth, "Thinking inside the box: Using appearance models and context based on room geometry," in Proc. Eur. Conf. Comp. Vis., 2010.
3. D. C. Lee, A. Gupta, M. Hebert, and T. Kanade, "Estimating spatial layout of rooms using volumetric reasoning about objects and surfaces," in Proc. Adv. Neural Inf. Process. Syst., 2010.
4. A. Gupta, A. A. Efros, and M. Hebert, "Blocks world re-visited: Image understanding using qualitative geometry and mechanics," in Proc. Eur. Conf. Comp. Vis., 2010.
5. K. Karsch, C. Liu, and S. B. Kang, "Depthtransfer: Depth extraction from video using non-parametric sampling," IEEE Trans. Pattern Anal. Mach. Intell., 2014.
6. B. C. Russell and A. Torralba, "Building a database of 3d scenes from user annotations," in Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2009.
7. B. Liu, S. Gould, and D. Koller, "Single image depth estimation from predicted semantic labels," in Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2010.
8. L. Ladick, J. Shi, and M. Pollefeys, "Pulling things out of perspective," in Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2014.
9. J. D. Lafferty, A. McCallum, and F. C. N. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proc. Int. Conf. Mach. Learn., 2001.
10. T. Qin, T.-Y. Liu, X.-D. Zhang, D.-S. Wang, and H. Li, "Global ranking using continuous conditional random fields," in Proc. Adv. Neural Inf. Process. Syst., 2008.
11. V. Radosavljevic, S. Vucetic, and Z. Obradovic, "Continuous conditional random fields for regression in remote sensing," in Proc. Eur. Conf. Artificial Intell., 2010.
12. K. Ristovski, V. Radosavljevic, S. Vucetic, and Z. Obradovic, "Continuous conditional random fields for efficient regression in large fully connected graphs," in AAAI Conf. Artifi-cial Intell., 2013.
13. A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, "CNN features off-the-shelf: An astounding baseline for recognition," in Proc. Workshop of IEEE Conf. Comp. Vis. Patt. Recogn., June 2014.
14. A. Saxena, S. H. Chung, and A. Y. Ng, "Learning depth from single monocular images," in Proc. Adv. Neural Inf. Process. Syst., 2005.
15. A. Saxena, M. Sun, and A. Y. Ng, "Make3d: Learning 3d scene structure from a single still image," IEEE Trans. Pat-tern Anal. Mach. Intell., 2009.
16. M. Liu, M. Salzmann, and X. He, "Discrete-continuous depth estimation from a single image," in Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2014.
17. J. Tompson, A. Jain, Y. LeCun, and C. Bregler, "Joint training of a convolutional network and a graphical model for human pose estimation," in Proc. Adv. Neural Inf. Process. Syst., 2014.
18. T. Baltrusaitis, P. Robinson, and L. Morency, "Continuous conditional neural fields for structured regression," in Proc. Eur. Conf. Comp. Vis., 2014.
19. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst., 2012.
20. T. Ojala, M. Pietikainen, and D. Harwood, "Performance evaluation of texture measures with classification based on kullback discrimination of distributions," in Proc. IEEE Int. Conf. Patt. Recogn., 1994.
21. A. Vedaldi, "MatConvNet," http://www. vlfeat. org/matconvnet/, 2013.
22. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, "Return of the devil in the details: Delving deep into convolutional nets," in Proc. British Machine Vis. Conf., 2014.
23. P. K. Nathan Silberman, Derek Hoiem and R. Fergus, "Indoor segmentation and support inference from rgbd images," in Proc. Eur. Conf. Comp. Vis., 2012.
24. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, "SLIC superpixels compared to state-of-theart superpixel methods," IEEE Trans. Pattern Anal. Mach. Intell., 2012.