{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:04:54Z","timestamp":1786122294458,"version":"build-2736575974"},"reference-count":43,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276242"],"award-info":[{"award-number":["62276242"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62276242"],"award-info":[{"award-number":["62276242"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Hefei Municipal Natural Science Foundation","award":["HZR2431"],"award-info":[{"award-number":["HZR2431"]}]},{"name":"CAAI-MindSpore Open Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Monocular per-object distance estimation aims to predict a metric distance for each detected object from a single RGB image. Although monocular dense depth estimation and monocular 3D object detection are well studied, indoor object-level distance estimation remains weakly standardized. In dense depth-based pipelines, object distance is commonly obtained by combining object detection with depth prediction and aggregating depth values within a region of interest. However, existing approaches differ in region selection, detection source, and aggregation strategy, limiting comparability across methods. This paper proposes a unified, deployment-aligned evaluation protocol for dense depth-based pipelines, where both predicted and reference distances are computed within the same predicted bounding box. This formulation standardizes region-of-interest selection, removes dependence on ground-truth boxes at inference time, and enables consistent evaluation across late-fusion methods. The framework integrates a curriculum-trained Depth Anything V2 ViT-S model for metric depth estimation with a YOLO11n detector for object localization. Under the same-box evaluation protocol on SUN RGB-D, the Depth Anything V2 ViT-S backbone achieves object-wise distance estimation accuracy of MAE = 0.1286 m, RMSE = 0.1817 m, AbsRel = 0.0662, and \u03b41 = 0.9785 using mean aggregation over valid box depths. Scaling the backbone from ViT-S to ViT-L further improves performance to MAE = 0.1059 m, RMSE = 0.1484 m, AbsRel = 0.0553, and \u03b41 = 0.9898, corresponding to approximately 17.7% lower MAE and 18.3% lower RMSE relative to ViT-S, and provides a standardized reference point for indoor object-level distance evaluation under the same-box protocol.<\/jats:p>","DOI":"10.3390\/s26165013","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T09:14:28Z","timestamp":1786094068000},"page":"5013","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Unified Evaluation Protocol and Late-Fusion System for Monocular Per-Object Distance Estimation in Indoor Scenes"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9005-7153","authenticated-orcid":false,"given":"Adnan","family":"Ali","sequence":"first","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3197-8103","authenticated-orcid":false,"given":"Yu","family":"Jun","sequence":"additional","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, L., Kang, B., Huang, Z., Zhao, Z., Xu, X., Feng, J., and Zhao, H. (2024, January 10\u201315). Depth Anything V2. Proceedings of the 38th International Conference on Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada.","DOI":"10.52202\/079017-0688"},{"key":"ref_2","unstructured":"Bochkovskii, A., Delaunoy, A., Germain, H., Santos, M., Zhou, Y., Richter, S.R., and Koltun, V. (2025, January 24\u201328). Depth Pro: Sharp Monocular Metric Depth in Less Than a Second. Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), Singapore."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., and Koltun, V. (2021, January 11\u201317). Vision Transformers for Dense Prediction. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Montr\u00e9al, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2354","DOI":"10.1109\/TPAMI.2025.3628473","article-title":"UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler","volume":"48","author":"Piccinelli","year":"2026","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","unstructured":"Bhat, S.F., Birkl, R., Wofk, D., Wonka, P., and M\u00fcller, M. (2023). ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"132274","DOI":"10.1016\/j.eswa.2026.132274","article-title":"MonoSTR: Structure-aware monocular 3D object detection via keypoint-based geometric representation","volume":"321","author":"Cho","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yang, X., Pu, F., Liao, Q., and Yang, W. (2026). Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3-D Object Detection. arXiv.","DOI":"10.1109\/TITS.2026.3659175"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nguyen Canh, T., Trinh, Q.M., Dang, T.V., Tan, P.X., and HoangVan, X. (2025). Refined 3D Object Localization with Monocular Camera using Depth Estimation and Geometric Refinement. Interactive Collaborative Robotics, Springer.","DOI":"10.1007\/978-3-032-11903-2_19"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, R., Choi, H.-S., Jung, D., Anh, P.H.N., Jeong, S.-K., and Zhu, Z. (2025). AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features. Appl. Sci., 15.","DOI":"10.3390\/app15137538"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.1109\/TPAMI.2025.3529084","article-title":"Exploiting Ground Depth Estimation for Mobile Monocular 3D Object Detection","volume":"47","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104303","DOI":"10.1016\/j.cviu.2025.104303","article-title":"Monocular per-object distance estimation with Masked Object Modeling","volume":"253","author":"Panariello","year":"2025","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_12","unstructured":"Zhu, J., and Fang, Y. (November, January 27). Learning object-specific distance from a monocular image. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shahzad, M.Z., Hanif, M.A., and Shafique, M. (2024, January 14\u201318). DECADE: Towards Designing Efficient-yet-Accurate Distance Estimation Modules for Collision Avoidance in Mobile Advanced Driver Assistance Systems. Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/IROS58592.2024.10801667"},{"key":"ref_14","unstructured":"Tu, Z., Huang, X., He, Y., Zhou, R., Du, B., and Wu, W. (2025). Depth as Points: Center Point-based Depth Estimation. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yu, J., and Choi, H. (2021). YOLO MDE: Object Detection with Monocular Depth Estimation. Electronics, 11.","DOI":"10.3390\/electronics11010076"},{"key":"ref_16","unstructured":"Eigen, D., Puhrsch, C., and Fergus, R. (2014, January 8\u201313). Depth Map Prediction from a Single Image using a Multi-Scale Deep Network. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Silberman, N., Kohli, P., Hoiem, D., and Fergus, R. (2012, January 7\u201313). Indoor segmentation and support inference from RGBD images. Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy.","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"ref_18","unstructured":"Bhat, S.F., Alhashim, I., and Wonka, P. (2021, January 19\u201325). AdaBins: Depth Estimation using Adaptive Bins. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shao, S., Pei, Z., Wu, X., Liu, Z., Chen, W., and Li, Z. (2023, January 10\u201316). IEBins: Iterative Elastic Bins for Monocular Depth Estimation. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA.","DOI":"10.52202\/075280-2307"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yuan, W., Gu, X., Dai, Z., Zhu, S., and Tan, P. (2022, January 19\u201324). Neural Window Fully-connected CRFs for Monocular Depth Estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00389"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., and Zhao, H. (2024, January 16\u201322). Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00987"},{"key":"ref_22","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., and El-Nouby, A. (2024). DINOv2: Learning Robust Visual Features without Supervision. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10579","DOI":"10.1109\/TPAMI.2024.3444912","article-title":"Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-Shot Metric Depth and Surface Normal Estimation","volume":"46","author":"Hu","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Piccinelli, L., Yang, Y.H., Sakaridis, C., Segu, M., Li, S., Gool, L.V., and Yu, F. (2024). UniDepth: Universal Monocular Metric Depth Estimation. arXiv.","DOI":"10.1109\/CVPR52733.2024.00963"},{"key":"ref_25","unstructured":"Geiger, A., Lenz, P., Stiller, C., and Urtasun, R. (2026, July 23). The KITTI Vision Benchmark Suite. Int. J. Robot. Res., Available online: https:\/\/www.cvlibs.net\/datasets\/kitti\/eval_depth_all.php."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13\u201319). nuscenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Brazil, G., Kumar, A., Julian, S., Ravi, N., Johnson, J., and Gkioxari, G. (2023, January 17\u201324). OMNI3D: A Large Benchmark and Model for 3D Object Detection in the Wild. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01264"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"De Guzman, L.B.N., and Raymond See, A. (2023, January 13\u201316). Using Monocular Depth Estimation for Distance Estimation in a Moving Vehicle. Proceedings of the 2023 International Conference on Computer Graphics and Image Processing (CGIP), Tokyo, Japan.","DOI":"10.1109\/CGIP58526.2023.00012"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Thombre, A., Rai, A.K., Dumka, L., and Agarwal, A. (2024, January 12\u201314). Object Distance Estimation from a Single Moving Camera for Advanced Driver Assistance System. Proceedings of the IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT62155.2024.10677218"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"\u00c7etin, E., Sar\u0131, T.T., Assoy, M., and Se\u00e7inti, G. (2024, January 10\u201313). Monodepth-Based Object Detection and Depth Sensing for Vehicle Vision Systems. Proceedings of the IEEE World Forum on Internet of Things (WF-IoT), Ottawa, ON, Canada.","DOI":"10.1109\/WF-IoT62078.2024.10811238"},{"key":"ref_31","first-page":"325","article-title":"Absolute Distance Prediction Based on Deep Learning Object Detection and Monocular Depth Estimation Models","volume":"Volume 339","author":"Masoumian","year":"2021","journal-title":"Proceedings of the Frontiers in Artificial Intelligence and Applications"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Niccoli, N., Seidenari, L., Greco, I., and Rovero, F. (2026). Benchmark on Monocular Metric Depth Estimation in Wildlife Setting. Image Analysis and Processing (ICIAP), Springer.","DOI":"10.1007\/978-3-032-11381-8_44"},{"key":"ref_33","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled Weight Decay Regularization. Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_34","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancement. arXiv."},{"key":"ref_35","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2026, July 23). Ultralytics YOLO. Available online: https:\/\/zenodo.org\/records\/17566701."},{"key":"ref_36","unstructured":"MMDetection3D Contributors (2026, July 23). MMDetection3D OpenMMLab Next-Generation Platform for General 3D Object Detection. Software. Available online: https:\/\/github.com\/open-mmlab\/mmdetection3d."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Roberts, M., Ramapuram, J., Ranjan, A., Kumar, A., Bautista, M.A., Paczan, N., Webb, R., and Susskind, J.M. (2021, January 11\u201317). Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01073"},{"key":"ref_38","unstructured":"Yeshwanth, C., Liu, Y.C., Nie\u00dfner, M., and Dai, A. (2023, January 22\u201329). ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Song, S., Lichtenberg, S.P., and Xiao, J. (2015, January 7\u201312). SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298655"},{"key":"ref_40","unstructured":"Vasiljevic, I., Kolkin, N.I., Zhang, S., Luo, R., Wang, H., Dai, F.Z., Daniele, A.F., Mostajabi, M., Basart, S., and Walter, M.R. (2019). DIODE: A Dense Indoor and Outdoor Depth Dataset. arXiv."},{"key":"ref_41","first-page":"9146","article-title":"From big to small: Multi-scale local planar guidance for monocular depth estimation","volume":"44","author":"Lee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"ref_42","unstructured":"Kim, D., Ka, W., Ahn, P., Joo, D., Chun, S., and Kim, J. (2022). Global-local path networks for monocular depth estimation with vertical cutdepth. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3964","DOI":"10.1109\/TIP.2024.3416065","article-title":"BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation","volume":"33","author":"Li","year":"2024","journal-title":"IEEE Trans. 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