{"id":12957,"date":"2026-10-09T14:33:10","date_gmt":"2026-10-09T06:33:10","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=12957"},"modified":"2026-10-09T21:58:44","modified_gmt":"2026-10-09T13:58:44","slug":"jase-202701-36-017","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202701-36-017","title":{"rendered":"Research on Target Visual Depth Estimation in Highly Reflective Bin-Picking Scenarios"},"content":{"rendered":"\n
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<\/i> 2027<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 36<\/a><\/p>\n<\/div>\n\n\n\n

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Yunhui Zhang, Qi Yang<\/i><\/a>, Minghao Li, Hui Han, and Yupu Zhang<\/p>\n\n\n\n

School of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, China<\/p>\n<\/div>\n\n\n\n

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Received: August 18, 2026
Accepted: September 20, 2026
Publication Date: October 09, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

The proposed Geometric-Radiometric Collaborative Framework <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202701_36.017<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Accurate monocular metric depth perception is crucial for complex robotic tasks such as bin-picking. However, in real industrial scenes, specular reflections from metal surfaces and mutual occlusions cause severe feature distortions and scale inconsistencies. To address this issue, we propose a Geometric-Radiometric Collaborative Framework guided by specular failure priors for robust absolute metric reconstruction under strong specular reflections. We decouple canonical depth reconstruction from global metric calibration and use affine parameters s and t to map scale-independent canonical depth to metric depth. In addition, a specular-aware energy constraint combining mask guidance and image gradients suppresses reflection-induced depth collapse and local distortions. Experiments on the XYZ-IBD dataset demonstrate that our method consistently outperforms the evaluated baseline models[cite: 99]. In highly reflective metal regions, the framework lowers the root mean square error (RMSE) to 97.0 mm, increases the \u03b4\u2081 accuracy to 91.39%, and reduces the Local Surface Smoothness (LSS) metric to 3.04. These results provide a reliable basis for 3D reconstruction in vision-based robotic bin-picking.<\/p>\n\n\n\n

Keywords: Monocular depth estimation, Absolute scale recovery, Energy functional optimization, Specular reflection, Robotic bin-picking<\/em><\/p>\n\n\n\n

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  1. [1] A. Cordeiro, L. F. Rocha, J. Boaventura-Cunha, D. Figueiredo, and J. P. Souza, (2026) “A Review of Visual Perception for Robotic Bin-Picking” Robotics and Autonomous Systems 196: 105236. DOI: https:\/\/doi.org\/10.1016\/j.robot.2025.105236.<\/li>\n
  2. [2] R. L. Haugaard and A. G. Buch. “SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings”. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2022, 6739-6748. DOI: https:\/\/doi.org\/10.1109\/CVPR52688.2022.00663.<\/li>\n
  3. [3] J. Huang, J. Hu, P. K. Yu, S. Ilic, M. Sundermeyer, and B. Busam. “XYZ-IBD: Benchmarking Robust 6D Object Pose Estimation under Real-World Industrial Complexity”. In: European Conference on Computer Vision (ECCV). 2026.<\/li>\n
  4. [4] R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, (2022) “Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer” IEEE Transactions on Pattern Analysis and Machine Intelligence 44(3): 1623-1637. DOI: https:\/\/doi.org\/10.1109\/TPAMI.2020.3019967.<\/li>\n
  5. [5] A. Eftekhar, A. Sax, J. Malik, and A. Zamir. “Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets From 3D Scans”. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). 2021, 10766-10776. DOI: https:\/\/doi.org\/10.1109\/ICCV48922.2021.01061.<\/li>\n
  6. [6] W. Yin, J. Zhang, O. Wang, S. Niklaus, L. Mai, S. Chen, and C. Shen. “Learning to Recover 3D Scene Shape from a Single Image”. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021, 204-213. DOI: https:\/\/doi.org\/10.1109\/CVPR46437.2021.00027.<\/li>\n
  7. [7] L. Piccinelli, C. Sakaridis, Y.-H. Yang, M. Segu, S. Li, W. Abbeloos, and L. Van Gool, (2026) “UniDepth V2: Universal Monocular Metric Depth Estimation Made Simpler” IEEE Transactions on Pattern Analysis and Machine Intelligence 48(3): 2354-2367. DOI: https:\/\/doi.org\/10.1109\/TPAMI.2025.3628473.<\/li>\n
  8. [8] J. Yang, J. Yao, and S. L. Waslander. “Active Pose Refinement for Textureless Shiny Objects using the Structured Light Camera”. In: 2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024, 7550-7557. DOI: https:\/\/doi.org\/10.1109\/IROS58592.2024.10801300.<\/li>\n
  9. [9] S. F. Bhat, I. Alhashim, and P. Wonka. “AdaBins: Depth Estimation Using Adaptive Bins”. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021, 4009-4018. DOI: https:\/\/doi.org\/10.1109\/CVPR46437.2021.00400.<\/li>\n
  10. [10] A. Costanzino, P. Z. Ramirez, M. Poggi, F. Tosi, S. Mattoccia, and L. Di Stefano. “Learning Depth Estimation for Transparent and Mirror Surfaces”. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV). 2023, 9210-9221. DOI: https:\/\/doi.org\/10.1109\/ICCV51070.2023.00848.<\/li>\n
  11. [11] W. Choi, K. Hwang, M. Choi, K. Han, W. Choi, M. Shin, and S. Im, (2025) “Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces” Proceedings of the AAAI Conference on Artificial Intelligence 39(3): 2555-2563. DOI: https:\/\/doi.org\/10.1609\/aaai.v39i3.32258.<\/li>\n
  12. [12] K. He, J. Sun, and X. Tang, (2013) “Guided Image Filtering” IEEE Transactions on Pattern Analysis and Machine Intelligence 35(6): 1397-1409. DOI: https:\/\/doi.org\/10.1109\/TPAMI.2012.213.<\/li>\n
  13. [13] F. Liu, C. Shen, and G. Lin. “Deep Convolutional Neural Fields for Depth Estimation from a Single Image”. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015, 5162-5170. DOI: https:\/\/doi.org\/10.1109\/CVPR.2015.7299152.<\/li>\n
  14. [14] S. A. Shafer, (1985) “Using Color to Separate Reflection Components” Color Research & Application 10(4): 210-218. DOI: https:\/\/doi.org\/10.1002\/col.5080100409.<\/li>\n
  15. [15] J. M. Facil, B. Ummenhofer, H. Zhou, L. Montesano, T. Brox, and J. Civera. “CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth”. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019, 11818-11827. DOI: https:\/\/doi.org\/10.1109\/CVPR.2019.01210.<\/li>\n
  16. [16] X. Cheng, P. Wang, and R. Yang. “Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network”. In: European Conference on Computer Vision (ECCV). Springer International Publishing, 2018, 108-125. DOI: https:\/\/doi.org\/10.1007\/978-3-030-01270-0_7.<\/li>\n
  17. [17] M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim. “Visual Prompt Tuning”. In: European Conference on Computer Vision (ECCV). Springer Nature Switzerland, 2022, 709-727. DOI: https:\/\/doi.org\/10.1007\/978-3-031-19827-4_41.<\/li>\n
  18. [18] L. Yang, B. Kang, Z. Huang, Z. Zhao, X. Xu, J. Feng, and H. Zhao. “Depth Anything V2”. In: Advances in Neural Information Processing Systems. 37. Curran Associates, Inc., 2024, 21875-21877. DOI: https:\/\/doi.org\/10.52202\/079017-0688.<\/li>\n
  19. [19] S. F. Bhat, R. Birkl, D. Wofk, P. Wonka, and M. M\u00fcller, (2023) “ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth” arXiv preprint arXiv:2302.12288: DOI: https:\/\/doi.org\/10.48550\/ARXIV.2302.12288.<\/li>\n
  20. [20] J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg. “Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics”. In: Robotics: Science and Systems (RSS). 2017. DOI: https:\/\/doi.org\/10.15607\/rss.2017.xiii.058.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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