The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Together, these contributions yield state-of-the-art monocular and stereo self-supervised depth estimation results on the KITTI dataset [13], and simplify many components found in the existing top performing models.”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [13]
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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
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
Cited by
Sorted by citations
Items 101–143 of 143
Fusing LiDAR and Color Imagery for Object Detection using Convolutional Neural Networks
Farahnakian, F., & Heikkonen, J. (2020) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion45008.2020.9190620 · Cited by 4
Edge Enhanced Scene Understanding and Depth Estimation
Islam, N. U., & Shah, S. H. (2025) · International Conference on Image, Video and Signal Processing (IVSP) · doi:10.1145/3749859.3749861 · Cited by 3
Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation
Atapour–Abarghouei, A., & Breckon, T. P. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-28603-3_2 · Cited by 3
Singh, M., & Goyal, P. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116588 · Cited by 3
Stepcenkov, S., Wilhelm, T., & Wöhler, C. (2022) · Remote Sensing · doi:10.3390/rs14143457 · Cited by 3
Design of Knowledge Map Construction Based on Convolutional Neural Network
Li, X., Chen, M., Xie, G., & Jiang, Y. (2019) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800141951008x · Cited by 3
Image dehazing with uneven illumination prior by dense residual channel attention network
Yin, S., Jin, X., Wang, Y. N., & Basu, A. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.0873 · Cited by 3
Yu, X., Franchi, G., & Aldea, E. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897930 · Cited by 3
Input-Perturbation-Sensitivity for Performance Analysis of CNNS on Image Recognition
Rao, Z., He, M., & Zhu, Z. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803012 · Cited by 3
Self-citationMDEAN: Multi-View Disparity Estimation with an Asymmetric Network
Pei, Z., Wen, D., Zhang, Y., Ma, M., Guo, M., Zhang, X., & Yang, Y. (2020) · Electronics · doi:10.3390/electronics9060924 · Cited by 3
CI-Net: Contextual Information for Joint Semantic Segmentation and Depth EstimationPreprint
Gao, T., Wu, W., Cai, Z., Fan, Z., Xie, S. Q., Wang, X., & Yu, Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.13800 · Cited by 3
DDConv: Dynamic Dilated Convolution
Hu, H., Yu, C., Zhou, Q., Guan, Q., & Zhou, T. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3593177 · Cited by 3
Amirkolaee, H. A., & Arefi, H. (2019) · Journal of Geospatial Information Technology · doi:10.29252/jgit.7.3.173 · Cited by 2
Multi-Scale Context Enhanced Network for Monocular Depth Estimation
Wang, W., & Ning, Y. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1848/1/012023 · Cited by 2
DE1-SoC FPGA Support for Human Posture Detection System
Hock, C. J., & Ali, A. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-8129-5_13 · Cited by 2
Multi-Task Label Discovery via Hierarchical Task Tokens for Partially Annotated Dense Predictions
Zhang, J., Ye, H., Li, X. S., Wang, W., & Xu, D. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755727 · Cited by 2
Evaluating Image Quality of DIBR-synthesized Views in Texture and Depth Domains
Wang, G., Guo, N., Liu, H., Zhang, Y., Zhou, Y., & Gu, K. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9728706 · Cited by 2
3D Human Pose Estimation Based on Volumetric Joint Coordinates
万, 云., Song, Y., & Liu, L. (2022) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2022.19167 · Cited by 1
MVI‐Depth: Multi‐View Indoor Depth Estimation Based on the Fusion of Semantic Information
Zhu, Y., Chen, B., Liu, H., & Li, X. (2025) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.70061 · Cited by 1
Research on Endoscopic Image Depth Estimation Method Based on Deep Learning
Wang, X., & Meng, X. (2023) · International Conference on Artificial Intelligence and Blockchain Technology (AIBT) · doi:10.1109/aibt57480.2023.00013 · Cited by 1
A Deep Joint Network for Monocular Depth Estimation Based on Pseudo-Depth Supervision
Tan, J., Gao, M. Z., Duan, T., & Gao, X. (2023) · Mathematics · doi:10.3390/math11224645 · Cited by 1
Depth Estimation Using Weighted-Loss and Transfer Learning
Hafeez, M. A., Madden, M. G., Sistu, G., & Ullah, I. (2024) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0012461300003660 · Cited by 1
Lee, C. Y., Kim, D. J., Suh, Y.-J., & Hwang, D. K. (2024) · IEEE Access · doi:10.1109/access.2024.3523912 · Cited by 1
Unsupervised Monocular Depth Estimation CNN Robust to Training Data Diversity
Anisimovskiy, V., Shcherbinin, A., Turko, S., & Kurilin, I. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47358-7_4 · Cited by 1
Dynamic Guided Network for Monocular Depth Estimation
Xing, X., Cai, Y., Wang, Y., Lü, T., Yang, Y., & Wen, D. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413264 · Cited by 1
Learning Depth for Multi-View Stereo with Adversarial Training
Wang, L., Fan, D., & Li, J. (2021) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc52312.2021.9602653 · Cited by 1
Distortion-Free Scanning and Copying of Bound Documents
Safonov, I. V., Kurilin, I. V., Rychagov, M. N., & Tolstaya, E. V. (2019) · Signals and communication technology · doi:10.1007/978-3-030-05342-0_1
Monocular-SLAM dense mapping algorithm and hardware architecture for FPGA acceleration
Aguilar‐González, A. (2019) · HAL · doi:10.70675/52192c81ze877z4a68zb358z7cfd9bd80300
An Improved Convolutional Neural Network for Monocular Depth Estimation
Kang, J. X., Dang, A., Zhang, B., Wang, Y., Su, H., Su, F., Ci, T., & Wang, F. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-0644-4_94
Learning to Predict the 3D Layout of a ScenePreprint
Lin, J. A., Brünker, J., & Fährmann, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09977
Predicting Depth from Semantic Segmentation using Game Engine DatasetPreprint
Kashi, M. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15257
Mendes, R. D. Q. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.18.2021.tde-02092021-110320
Efficient Monocular Depth Estimation with Transfer Feature Enhancement
Yin, M. (2021) · International Journal of Circuits Systems and Signal Processing · doi:10.46300/9106.2021.15.127
Monocular Depth Matching With Hybrid Sampling and Depth Label Propagation
Hua, Y., Long, Q. X., & Jin, L. Z. (2022) · International Journal of Digital Crime and Forensics · doi:10.4018/ijdcf.302879
Mekhzoum, H., Bourja, O., Abdelali, H. A., & Derrouz, H. (2022) · Research Square · doi:10.21203/rs.3.rs-1827874/v1
Mekhzoum, H., Bourja, O., Abdelali, H. A., & Derrouz, H. (2022) · Research Square · doi:10.21203/rs.3.rs-1827874/v2
A Novel Capability of Object Identification and Recognition Based on Integrated mWMM
Sarwar, M. Z., Alatiyyah, M. H., Jalal, A., Shorfuzzaman, M., Alsufyani, N., & Park, J. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.035442
王, 晓. (2024) · Journal of Image and Signal Processing · doi:10.12677/jisp.2024.132010
A Survey of Monocular Depth Estimation based on Deep Learning
Song, Z., & Jianxing, W. (2024) · Frontiers in Computing and Intelligent Systems · doi:10.54097/7iuoscdg
Zengyu, 田. T., Changku, 孙. S., Yue, 李. L., Luhua, 付. F., & Peng, 王. W. (2025) · Laser & Optoelectronics Progress · doi:10.3788/lop250436
Haiyang, 胡. H., Chaoping, 陈. C., Tianmu, 高. G., Baoen, 韩. H., Yunfan, 杨. Y., Yi, 刘. L., & Xiaojun, 武. W. (2025) · Infrared and Laser Engineering · doi:10.3788/irla20250157
Uncertainty quantification for vision regression tasks
Yu, X. (2023) · theses.fr (ABES) · doi:10.70675/ea582b0az7992z4ea8zbf03z375e720e871f
Zioulis, N., Karakottas, A., Dimitrios, Z., & Petros, D. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1311717
Metadata
- Authors
- Li, B., Dai, Y., & He, M.
- Affiliations
- Northwestern Polytechnical University (CN)
- Corresponding author
- Dai, Yuchao
- He, Mingyi
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 83
- Pages
- 328-339
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2964193874
- Cited by
- 142
- References
- 45
- Citations by year
- 2025: 7 · 2024: 12 · 2023: 12 · 2022: 18 · 2021: 21 · 2020: 31 · 2019: 33 · 2018: 8
- FWCI
- 11.38
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Pattern Recognition (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Natural Science Foundation of China (61420106007, 61671387)
- Australian Research Council (DE140100180)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
- Keywords
- monocular depth estimation, dilated convolutional neural network, hierarchical feature fusion, quantization error reduction, semantic segmentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review