[논문]Single Image Depth Estimation With Integration of Parametric Learning and Non-Parametric Sampling

Jung, Hyungjoo; Sohn, Kwanghoon

doi:10.9717/kmms.2016.19.9.1659

[국내논문] Single Image Depth Estimation With Integration of Parametric Learning and Non-Parametric Sampling 원문보기

멀티미디어학회논문지 = Journal of Korea Multimedia Society, v.19 no.9, 2016년, pp.1659 - 1668

Jung, Hyungjoo (School of Electrical & Electronic Engineering, Yonsei University) , Sohn, Kwanghoon (School of Electrical & Electronic Engineering, Yonsei University)

Abstract ▼ AI-Helper

Understanding 3D structure of scenes is of a great interest in various vision-related tasks. In this paper, we present a unified approach for estimating depth from a single monocular image. The key idea of our approach is to take advantages both of parametric learning and non-parametric sampling method. Using a parametric convolutional network, our approach learns the relation of various monocular cues, which make a coarse global prediction. We also leverage the local prediction to refine the global prediction. It is practically estimated in a non-parametric framework. The integration of local and global predictions is accomplished by concatenating the feature maps of the global prediction with those from local ones. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

Keyword

AI 본문요약
AI-Helper

제안 방법

The proposed method consists of three components as follows: parametric model for coarse depth estimation, non-parametric sampling framework for warped depth maps, and CNN layers for depth refinement. The overall framework of the proposed method is shown in Fig.

이론/모형

This paper has presented depth estimation method from a single monocular scene using popular FCN and non-parametric method. FCN can extract globally plausible depth, whereas outputs of non-parametric method preserve local structures, due to warping process based on Patch Match.

성능/효과

This paper has presented depth estimation method from a single monocular scene using popular FCN and non-parametric method. FCN can extract globally plausible depth, whereas outputs of non-parametric method preserve local structures, due to warping process based on Patch Match. Each virtues is aggregated by CNN, which only learn residual.
Each virtues is aggregated by CNN, which only learn residual. Since we adapt the newest techniques (i.e., batch normalization and residual net) in our deep networks learning, they converge fast and produce better results. Experimental results show that the proposed method can outperform other existing methods in estimating both of global and local structures of depth.
, batch normalization and residual net) in our deep networks learning, they converge fast and produce better results. Experimental results show that the proposed method can outperform other existing methods in estimating both of global and local structures of depth. In experiments, we tried to use large RGB-D dataset.
It is well-known that large number of training images make a good performance in deep neural networks, and it helps to avoid overfitting problem. Non-parametric methods can also make better performance in large database. However, large database makes it hard to retrieve training images in non-parametric method.
Non-parametric methods can also make better performance in large database. However, large database makes it hard to retrieve training images in non-parametric method. Fast image search algorithms can improve the proposed method.
However, large database makes it hard to retrieve training images in non-parametric method. Fast image search algorithms can improve the proposed method. Also, in future works, we will extend the proposed method by jointly training parametric and non-parametric methods.

후속연구

Fast image search algorithms can improve the proposed method. Also, in future works, we will extend the proposed method by jointly training parametric and non-parametric methods.

참고문헌 (32)

X. Ren, L. Bo, and D. Fox, "RGB-(D) Scene Labeling: Features and Algorithms," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition , pp. 2759-2766, 2012.
D. Lin, S. Fidler, and R. Urtasun, "Holistic Scene Understanding for 3D Object Detection with RGBD Cameras," Proceeding of IEEE International Conference on Computer Vision , pp. 1417- 1424, 2013.
J. Jeon, S. Cho, X. Tong, and S. Lee, "Intrinsic Image Decomposition Using Structure-Texture Separation and Surface Normals," Proceeding of European Conference on Computer Vision , pp. 218-233, 2014.
O. Wang, M. Lang, and M. Gross, "Stereo Brush: Interactive 2D to 3D Conversion using Discontinuous Warps," Proceedings of the 8th Eurographics Symposium on Sketch-based Interfaces and Modeling , pp. 47-54, 2011.
H. Yuan, S. Wu, P. Cheng, P. An, and S. Bao, "Nonlocal Random Walks Algorithms for Semi-Automatic 2D-to-3D Image Convertsion," IEEE Signal Processing Letters , Vol. 22, No. 3, pp. 371-374, 2015.

상세보기
J. Atick, P. Griffin, and N. Redlich, "Statistical Approach to Shape fomr Shading: Reconstruction of Three-Dimensional Face Surfaces from Single Two-Dimensional Images," Neural Computation , Vol. 8, No. 6, pp. 132-1340, 1996.

상세보기
R. Szeliski and P. Torr, "Geometrically Constrained Structure from Motion: Points on Planes," 3D Structure from Multiple Images of Large-Scale Environments , Springer Berlin Heideberg , pp. 171-186, 1998.
A. Oliva and A. Torralba, "Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope," International Journal of Computer Vision , Vol. 42, No. 3, pp. 145-175, 2001.
N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proceeding of IEEE Computer Vision and Pattern Recognition , Vol. 1, pp. 886-893, 2005.
K. Karsch, C. Liu, and S. Kang, "Depth Extraction from Video Using Non-parametric Sampling," Proceeding of European Conference on Computer Vision , pp. 775-788, 2012.
S. Choi, D. Min, B. Ham, Y. Kim, C. Oh, and K. Sohn, "Depth Analogy: Data-Driven Approach for Single Image Depth Estimation using Gradient Samples," IEEE Transaction on Image Processing , Vol. 24, No. 12, pp. 5953-5966, 2015.

상세보기
A. Saxena, M. Sun, and A. Ng, "MAKE3D: Learning 3D Scene Structure from a Single Still Image," IEEE Transaction on Pattern Analysis machine Intelligence , Vol. 31, No. 5, pp. 824-840, 2009.

상세보기
D. Eigen, C. Puhrsch, and R. Fergus, "Depth Map Prediction from a Single Image Using a Multi-Scale Deep Network," Advances in Neural Information Processing Systems , pp. 2366-2374, 2014.
F. Liu, C. Shen, and G. Lin, "Deep Convolutional Neural Fields for Depth Estimation from a Single Image," Proceeding of IEEE Conference on Computer Vision and Recognition , pp. 5162-5170, 2015.
J. Long, E. Shelhamer, and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition , pp. 3431-3440, 2015.
C. Liu, J. Yuen, and A. Torralba, "SIFT Flow: Depth Correspondence across Scenes and Its Applications," IEEE Transaction Patterns Analysis Machine Intelligence , Vol. 33, No. 33, pp. 978-994, 2011.

상세보기
C. Barnes, E. Shechtman, A. Finkelstein, and D. Goldman, "PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing," ACM Transactions on Graphics , Vol. 28, No. 3, pp. 24, 2009.

상세보기
J. Konrad, M. Wang, P. Ishwar, C. Wu, and D. Mukherjee, "Learning-based, Automatic 2D-to-3D Image and Video Conversion," IEEE Transaction Image Processing , Vol. 22, No. 9, pp. 3485-3496, 2013.

상세보기
L. C. Chen, G. Papandreou, I. Kokkonos, K. Murphy, and A.L. Yuille, "Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs," arXiv: 1412.7062, 2014.
L. C. Chen, J. Barron, and A. L. Yulle, "Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform," arXiv: 1511.03328, 2015.
F. Liu, C. Shen, G. Lin, and I. D. Reid, "Learning Depth from Single Monocular Images using Deep Convolutional Neural Fields," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition , pp. 5162-5170, 2015.
W. Chen, Z. Fu, D. Yang, and J. Dong, "Single-Image Depth Perception in the Wild" , arXiv: 1604.03901, 2016.
K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Reconstruction," arXiv: 1409.1556, 2014.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, and L. Fei-Fei, "ImageNet Large Scale Visual Recognition Challenge," International Journal of Computer Vision , Vol. 115, No. 3, pp. 211-252, 2015.

상세보기
A. Krizhevsky, I. Sutskever, and G. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Advances in Neural Information Processing Systems , pp. 1097-1105, 2012.
J. Kim, J. Lee, and K. Lee, "Accurate Image Super-Resolution Using Very Deep Convolutional Networks," arXiv: 1511.04587, 2015.
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, "Indoor Segmentation and Support Inference from RGBD Images," Proceeding of European Conference on Computer Vision , pp. 746-760, 2012.
S. Loffe and c. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," Proceeding of International Conference on Machine Learning , pp. 448-456, 2015.
J. Kopf, M.F. Cohen, D. Lischinski, and M. Uyttendaele, "Joint Bilateral Upsampling," ACM Transactions on Graphics , Vol. 26, No. 3, pp. 96, 2007.

상세보기
K. He, J. Sun, and X. Tang, "Guided Image Filtering," Proceeding of European Conference on Computer Vision , pp. 1-14, 2010.
D. Lee, and S. Kwon, "A Recognition Method for Moving Objects Using Depth and Color Information," Journal of Korea Multimedia Society , Vol. 19, No. 4, pp. 681- 688, 2016.

원문보기 상세보기
S. Kim, and H. Kang, "Semantic Segmentation of Indoor Scenes Using Depth Superpixel," Journal of Korea Multimedia Society , Vol. 19, No. 3, pp. 531-538, 2016.

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