The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Monocular Depth Estimation Using Neural Regression Forest
Abstract
This paper presents a novel deep architecture, called neural regression forest (NRF), for depth estimation from a single image. NRF combines random forests and convolutional neural networks (CNNs). Scanning windows extracted from the image represent samples which are passed down the trees of NRF for predicting their depth. At every tree node, the sample is filtered with a CNN associated with that node. Results of the convolutional filtering are passed to left and right children nodes, i.e., corresponding CNNs, with a Bernoulli probability, until the leaves, where depth estimations are made. CNNs at every node are designed to have fewer parameters than seen in recent work, but their stacked processing along a path in the tree effectively amounts to a deeper CNN. NRF allows for parallelizable training of all "shallow" CNNs, and efficient enforcing of smoothness in depth estimation results. Our evaluation on the benchmark Make3D and NYUv2 datasets demonstrates that NRF outperforms the state of the art, and gracefully handles gradually decreasing training datasets.
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Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 3×
“where zi = (xi, yi) is the pixel location in the image, hi is the HSV color histogram of window xi, and σz = 3 and σh = 30 control sensitivity [25].”
BackgroundMethodInfluentialIndoor 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
Learning Hierarchical Features for Scene Labeling
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620
Segmentation and Recognition Using Structure from Motion Point Clouds
Brostow, G. J., Shotton, J., Fauqueur, J., & Cipolla, R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_5 · Cited by 1,050
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
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
Ma, F., & Karaman, S. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460184 · Cited by 650
Loyola-Gonzalez, O. (2019) · IEEE Access · doi:10.1109/access.2019.2949286 · Cited by 559
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
From 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
Metadata
- Authors
- Roy, A., & Todorovic, S.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 5506-5514
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2436453945
- Cited by
- 318
- References
- 6
- FWCI
- 15.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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