Subject : Supervised monocular depth estimation works
有监督的单目深度估计
2014
- Eigen, D., Puhrsch, C., & Fergus, R. (2014). Depth map prediction from a single image using a multi-scale deep network. Advances in neural information processing systems, 27.[CNN进行室内外深度估计任务的开山之作][1406.2283] Depth Map Prediction from a Single Image using a Multi-Scale Deep Network (arxiv.org)
2015
- Eigen, D., & Fergus, R. (2015). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In Proceedings of the IEEE international conference on computer vision (pp. 2650-2658)[1411.4734] Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture (arxiv.org)
- Liu, F., Shen, C., & Lin, G. (2015). Deep convolutional neural fields for depth estimation from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5162-5170).CVPR 2015 Open Access Repository
2016
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Roy, A., & Todorovic, S. (2016). Monocular depth estimation using neural regression forest. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5506-5514).CVPR 2016 Open Access Repository
2017
- Xu, D., Ricci, E., Ouyang, W., Wang, X., & Sebe, N. (2017). Multi-scale continuous crfs as sequential deep networks for monocular depth estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5354-5362). CVPR 2017 Open Access Repository
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Ma, X., Geng, Z., & Bie, Z. (2017). Depth estimation from single image using CNN-residual network. SemanticScholar.http://cs231n.stanford.edu/reports/2017/pdfs/203.pdf
2018
- Fu, H., Gong, M., Wang, C., Batmanghelich, K., & Tao, D. (2018). Deep ordinal regression network for monocular depth estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2002-2011). [第一篇用分类思想进行有序回归的工作，有代表性]CVPR 2018 Open Access Repository
- Jiao, J., Cao, Y., Song, Y., & Lau, R. (2018). Look deeper into depth: Monocular depth estimation with semantic booster and attention-driven loss. In Proceedings of the European conference on computer vision (ECCV) (pp. 53-69). [多模态]ECCV 2018 Open Access Repository
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Zhang, Z., Cui, Z., Xu, C., Jie, Z., Li, X., & Yang, J. (2018). Joint task-recursive learning for semantic segmentation and depth estimation. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 235-251). [多模态]ECCV 2018 Open Access Repository
2019
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Wang, Y., Chao, W. L., Garg, D., Hariharan, B., Campbell, M., & Weinberger, K. Q. (2019). Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8445-8453).CVPR 2019 Open Access Repository
[第一篇把dense depth引入3D目标检测的文章，有代表性]
数据集：
KITTI The KITTI Vision Benchmark Suite
NYUv2 NYU Depth V2 « Nathan Silberman
感谢嘉宁师兄的整理，希望能帮助到刚进入这个领域的同学。