The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
Open accessAbstract
This paper addresses the problem of depth estimation from a single still image. Inspired by recent works on multi-scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. Through extensive experimental evaluation we demonstrate the effectiveness of the proposed approach and establish new state of the art results on publicly available datasets.
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213
Conditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386 · cited 4×
“It is worth noting that the normalization step in our mean-field updates for continuous CRFs is substantially different from that of discrete CRFs [36] based on a softmax function.”
BackgroundMethodInfluentialHypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511
Holistically-nested edge detection
Xie, S., & Tu, Z. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1004-z · Cited by 682
Hoiem, D., Efros, A. A., & Hebert, M. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073232 · Cited by 616
Discrete-Continuous Depth Estimation from a Single Image
Liu, M., Salzmann, M., & He, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.97 · Cited by 381
Cited by
Deep Ordinal Regression Network for Monocular Depth Estimation
Fu, H., Gong, M., Wang, C., Batmanghelich, K., & Tao, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00214 · Cited by 1,991
Xu, D., Ouyang, W., Wang, X., & Sebe, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00077 · Cited by 518
Self-citationFrom Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
Lee, J. H., Han, M.-K., Ko, D. W., & Suh, I. H. (2019) · arXiv · doi:10.48550/arxiv.1907.10326 · Cited by 507
Learning Depth with Convolutional Spatial Propagation Network
Cheng, X., Wang, P., & Yang, R. (2018) · arXiv · doi:10.48550/arxiv.1810.02695 · Cited by 345
Monocular Relative Depth Perception with Web Stereo Data Supervision
Xian, K., Shen, C., Cao, Z., Lu, H., Xiao, Y., Li, R., & Luo, Z. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00040 · Cited by 215
Metadata
- Authors
- Xu, D., Ricci, E., Ouyang, W., Wang, X., & Sebe, N.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 161-169
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2605938684
- Cited by
- 466
- References
- 9
- FWCI
- 17.96
- Type
- conference paper
- Language
- en
- Open access
- green
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- Xu, D., Ricci, E., Ouyang, W., Wang, X., & Sebe, N. (2017). Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.25
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