Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture
Open accessAbstract
In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a multiscale convolutional network that is able to adapt easily to each task using only small modifications, regressing from the input image to the output map directly. Our method progressively refines predictions using a sequence of scales, and captures many image details without any superpixels or low-level segmentation. We achieve state-of-the-art performance on benchmarks for all three tasks.
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Fisheye cameras are mounted low on a vehicle and are susceptible to lens soiling due to splashing of mud or water from the road.
“Fisheye cameras are mounted low on a vehicle and are susceptible to lens soiling due to splashing of mud or water from the road.”
From OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving (2021) · cites this paper as [7]
Visual Odometry (VO) is necessary for creating a map from the objects detected.
“Visual Odometry (VO) is necessary for creating a map from the objects detected [38].”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [38]
It is possible to estimate the depth of a scene (in effect, the map) from a single view solely as a function of the input image.
“and to estimate the depth of a scene (in effect, the map) from a single view solely as a function of the input image [27, 70, 158].”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [70]
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Vision based pixel-level bridge structural damage detection using a link ASPP network
Deng, W., Mou, Y., Kashiwa, T., Escalera, S., Nagai, K., Nakayama, K., Matsuo, Y., & Prendinger, H. (2020) · Automation in Construction · doi:10.1016/j.autcon.2019.102973 · Cited by 84 · see citing works
Traffic Scene Segmentation Based on RGB-D Image and Deep Learning
Li, L., Qian, B., Lian, J., Zheng, W., & Zhou, Y. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2724138 · Cited by 77 · see citing works
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
Mans Larsson, M., Stenborg, E., Hammarstrand, L., Pollefeys, M., Sattler, T., & Kahl, F. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00976 · Cited by 71 · see citing works
Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
Wang, Y., Sun, F., Lu, M., & Yao, A. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413621 · Cited by 68 · see citing works
Deep semantic classification for 3D LiDAR data
Dewan, A., Oliveira, G. L., & Burgard, W. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206198 · Cited by 60 · see citing works
Hard pixel mining for depth privileged semantic segmentation
Gu, Z., Niu, L., Zhao, H., & Zhang, L. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3035231 · Cited by 41 · see citing works
Multi-task Learning Using Multi-modal Encoder-Decoder Networks with Shared Skip Connections
Kuga, R., Kanezaki, A., Samejima, M., Sugano, Y., & Matsushita, Y. (2017) · 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw.2017.54 · Cited by 36 · see citing works
Multimodal Recurrent Neural Networks With Information Transfer Layers for Indoor Scene Labeling
Abdulnabi, A. H., Shuai, B., Zuo, Z., Chau, L.-P., & Wang, G. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2774007 · Cited by 33 · see citing works
AuxNet: Auxiliary Tasks Enhanced Semantic Segmentation for Automated Driving
Chennupati, S., Sistu, G., Yogamani, S., & Rawashdeh, S. (2019) · Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications · doi:10.5220/0007684106450652 · Cited by 42 · see citing works
Outdoor RGBD Instance Segmentation With Residual Regretting Learning
Xu, Z., Liu, S., Shi, J., & Lu, C. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2975711 · Cited by 20 · see citing works
Pagnutti, G., Minto, L., & Zanuttigh, P. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0502 · Cited by 14 · see citing works
Median Filter Helps Lymph Node Segmentation in Deep Learning via PET/CT
Zhang, X., Liao, W., & Xu, G. (2021) · 2021 4th International Conference on Digital Medicine and Image Processing · doi:10.1145/3506651.3506662 · Cited by 1 · see citing works
- Authors
- Eigen, D., & Fergus, R.
- Affiliations
- New York University (US)
- Meta (United States) (US)
- ORCID
- Fergus, Rob
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2650-2658
- OpenAlex
- W1905829557
- Cited by
- 2,862
- References
- 37
- Citations by year
- 2026: 57 · 2025: 156 · 2024: 220 · 2023: 266 · 2022: 302 · 2021: 398 · 2020: 394 · 2019: 419 · 2018: 314 · 2017: 215 · 2016: 103 · 2015: 17 · 2014: 1
- FWCI
- 88.86
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (CAREER)
- Office of Naval Research (N00014)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- depth estimation, surface normal estimation, semantic segmentation, multi-scale convolutional neural network, single-image 3D reconstruction, progressive refinement, multi-task learning
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