The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Single image depth estimation from predicted semantic labels
Abstract
We consider the problem of estimating the depth of each pixel in a scene from a single monocular image. Unlike traditional approaches, which attempt to map from appearance features to depth directly, we first perform a semantic segmentation of the scene and use the semantic labels to guide the 3D reconstruction. This approach provides several advantages: By knowing the semantic class of a pixel or region, depth and geometry constraints can be easily enforced (e.g., “sky” is far away and “ground” is horizontal). In addition, depth can be more readily predicted by measuring the difference in appearance with respect to a given semantic class. For example, a tree will have more uniform appearance in the distance than it does close up. Finally, the incorporation of semantic features allows us to achieve state-of-the-art results with a significantly simpler model than previous works.
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Cites
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Hoiem, D., Efros, A. A., & Hebert, M. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073232 · Cited by 616
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Deep convolutional neural fields for depth estimation from a single image
Liu, F., Shen, C., & Lin, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299152 · Cited by 892
CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction
Tateno, K., Tombari, F., Laina, I., & Navab, N. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.695 · Cited by 800
Semi-Supervised Deep Learning for Monocular Depth Map Prediction
Kuznietsov, Y., Stuckler, J., & Leibe, B. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.238 · Cited by 680
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling
Karsch, K., Liu, C., & Kang, S. B. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2316835 · Cited by 453
Discrete-Continuous Depth Estimation from a Single Image
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Video Object Segmentation without Temporal Information
Maninis, K.-K., Caelles, S., Chen, Y., Pont-Tuset, J., Leal-Taixe, L., Cremers, D., & Van Gool, L. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2838670 · Cited by 354
Joint Semantic Segmentation and 3D Reconstruction from Monocular Video
Kundu, A., Li, Y., Dellaert, F., Li, F., & Rehg, J. M. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10599-4_45 · Cited by 220
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
Luo, Y., Ren, J., Lin, M., Pang, J., Sun, W., Li, H., & Lin, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00024 · Cited by 205
Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference
Li, B., Dai, Y., & He, M. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.029 · Cited by 142
Li, W., & Hsu, C.-Y. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11070385 · Cited by 147
Single image depth estimation: An overview
Mertan, A., Duff, D. J., & Unal, G. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103441 · Cited by 121
Multi-modal Auto-Encoders as Joint Estimators for Robotics Scene Understanding
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Metadata
- Authors
- Liu, B., Gould, S., & Koller, D.
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 1253-1260
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2026203852
- Cited by
- 477
- References
- 2
- FWCI
- 14.57
- Type
- conference paper
- Language
- en
- Open access
- closed
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