{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:38:55Z","timestamp":1785411535210,"version":"3.56.0"},"reference-count":45,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003977","name":"Israel Science Foundation","doi-asserted-by":"publisher","award":["1519\/20"],"award-info":[{"award-number":["1519\/20"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"publisher"}]},{"award":["1519\/20"],"award-info":[{"award-number":["1519\/20"]}],"id":[{"id":"https:\/\/ror.org\/04sazxf24","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Depth estimation is an active area of research in computer vision. When restricted to passive imaging, it is frequently approached as a stereo matching problem. Due to significant advancements in deep learning, stereo matching models have seen substantial development. Most stereo matching models utilize supervised learning, relying on disparity maps as ground truth, typically obtained via active imaging systems. In this work, we introduce a passive multi-view framework for generating ground-truth depth data for stereo matching models using Computational Integral Imaging (CII). Using CII, we extract object depths from multi-view images obtained via a passive camera array and use these depths as training targets, eliminating the need for active depth acquisition. To improve the robustness and accuracy of CII-based depth extraction, we propose an object-aware formulation that incorporates pretrained object segmentation into the depth extraction process. This enables more reliable depth estimation for objects with complex appearances and challenging scene contexts such as occlusions. Furthermore, we exploit the camera array as a multi-stereo acquisition system, generating diverse stereo pairs with varying baselines and viewing orientations. The resulting training data expose stereo matching models to a broader range of geometric configurations than conventional stereo datasets. Our results demonstrate that this approach enhances the generalization capabilities of stereo matching models.<\/jats:p>","DOI":"10.3390\/s26134149","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T10:41:01Z","timestamp":1782902461000},"page":"4149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Object-Aware Computational Integral Imaging for Improved Object Depth Estimation and Stereo Matching Training"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8195-7369","authenticated-orcid":false,"given":"Daniel","family":"Vais","sequence":"first","affiliation":[{"name":"Department of Electro-Optics and Photonics Engineering, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4974-9683","authenticated-orcid":false,"given":"Yitzhak","family":"Yitzhaky","sequence":"additional","affiliation":[{"name":"Department of Electro-Optics and Photonics Engineering, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"ref_1","unstructured":"You, Y., Wang, Y., Chao, W.L., Garg, D., Pleiss, G., Hariharan, B., Campbell, M., and Weinberger, K.Q. (2020). Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. arXiv, arXiv:1906.06310."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Flacco, F., Kr\u00f6ger, T., De Luca, A., and Khatib, O. (2012, January 14\u201318). A depth space approach to human-robot collision avoidance. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6225245"},{"key":"ref_3","unstructured":"Han, J.J., Acar, A., Henry, C., and Wu, J.Y. (2024). Depth Anything in Medical Images: A Comparative Study. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8562323","DOI":"10.1155\/2020\/8562323","article-title":"Review of Stereo Matching Algorithms Based on Deep Learning","volume":"2020","author":"Zhou","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1016\/j.jksuci.2020.08.011","article-title":"Stereo matching algorithm based on deep learning: A survey","volume":"34","author":"Hamid","year":"2022","journal-title":"J. King Saud. Univ.\u2014Comput. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The KITTI dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_7","unstructured":"Scharstein, D., and Szeliski, R. (2003, January 18\u201320). High-accuracy stereo depth maps using structured light. Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., and Xia, T. (2017, January 21\u201326). Multi-view 3d object detection network for autonomous driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ku, J., Pon, A.D., and Waslander, S.L. (2019, January 15\u201320). Monocular 3d object detection leveraging accurate proposals and shape reconstruction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01214"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dong, H., Gu, W., Zhang, X., Xu, J., Ai, R., Lu, H., Kannala, J., and Chen, X. (2024, January 13\u201317). Superfusion: Multilevel lidar-camera fusion for long-range hd map generation. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10611320"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yildiz, A.S., Meng, H., and Swash, M.R. (2025). Real-Time Object Detection and Distance Measurement Enhanced with Semantic 3D Depth Sensing Using Camera\u2013LiDAR Fusion. Appl. Sci., 15.","DOI":"10.3390\/app15105543"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106695","DOI":"10.1016\/j.optlaseng.2021.106695","article-title":"Deep Learning Integral Imaging for Three-Dimensional Visualization, Object Detection, and Segmentation","volume":"146","author":"Yi","year":"2021","journal-title":"Opt. Lasers Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kadosh, M., and Yitzhaky, Y. (2023). 3D Object Detection via 2D Segmentation-Based Computational Integral Imaging Applied to a Real Video. Sensors, 23.","DOI":"10.3390\/s23094191"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6717","DOI":"10.1364\/AO.54.006717","article-title":"Automatic 3D object localization and isolation using computational integral imaging","volume":"54","author":"Aloni","year":"2015","journal-title":"Appl. Opt."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1109\/LPT.2014.2304883","article-title":"Detection of object existence from a single reconstructed plane obtained by integral imaging","volume":"26","author":"Aloni","year":"2014","journal-title":"IEEE Photonics Technol. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1109\/TPAMI.2006.70","article-title":"Adaptive support-weight approach for correspondence search","volume":"28","author":"Yoon","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","unstructured":"Eklundh, J.-O. (1994). Non-parametric local transforms for computing visual correspondence. Computer Vision\u2014ECCV \u201994, Springer."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s11263-006-7899-4","article-title":"Efficient Belief Propagation for Early Vision","volume":"70","author":"Felzenszwalb","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","unstructured":"Boykov, Y.Y., and Jolly, M.-P. (2001, January 7\u201314). Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. Proceedings of the Eighth IEEE International Conference on Computer Vision. ICCV 2001, Vancouver, BC, Canada."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TPAMI.1985.4767639","article-title":"Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming","volume":"PAMI-7","author":"Ohta","year":"1985","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","article-title":"Stereo Processing by Semiglobal Matching and Mutual Information","volume":"30","author":"Hirschmuller","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1014573219977","article-title":"A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms","volume":"47","author":"Scharstein","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., and Brox, T. (2016, January 27\u201330). A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.438"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zbontar, J., and LeCun, Y. (2015, January 7\u201312). Computing the Stereo Matching Cost with a Convolutional Neural Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298767"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chang, J.-R., and Chen, Y.-S. (2018, January 18\u201322). Pyramid Stereo Matching Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00567"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Guo, X., Yang, K., Yang, W., Wang, X., and Li, H. (2019, January 15\u201320). Group-Wise Correlation Stereo Network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00339"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, H., and Zhang, J. (2020, January 13\u201319). AANet: Adaptive Aggregation Network for Efficient Stereo Matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00203"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, F., Prisacariu, V., Yang, R., and Torr, P.H.S. (2019, January 15\u201320). GA-Net: Guided Aggregation Net for End-To-End Stereo Matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00027"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lipson, L., Teed, Z., and Deng, J. (2021, January 1\u20133). RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. Proceedings of the 2021 International Conference on 3D Vision (3DV), London, UK.","DOI":"10.1109\/3DV53792.2021.00032"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Seki, A., and Pollefeys, M. (2017, January 21\u201326). SGM-Nets: Semi-Global Matching with Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.703"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"109578","DOI":"10.1016\/j.optlaseng.2025.109578","article-title":"Hyperspectral images 3D reconstruction based on structure-from-motion and multi-view stereo","volume":"199","author":"Liu","year":"2026","journal-title":"Opt. Lasers Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5227","DOI":"10.1109\/TPAMI.2024.3362475","article-title":"SpectralGPT: Spectral Remote Sensing Foundation Model","volume":"46","author":"Hong","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6883","DOI":"10.1109\/TNNLS.2024.3401589","article-title":"Learning Disentangled Priors for Hyperspectral Anomaly Detection: A Coupling Model-Driven and Data-Driven Paradigm","volume":"36","author":"Li","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4245","DOI":"10.1007\/s11263-024-02331-0","article-title":"A Survey on Deep Stereo Matching in the Twenties","volume":"133","author":"Tosi","year":"2025","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"243","DOI":"10.26599\/CVM.2025.9450517","article-title":"Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation","volume":"12","author":"Xu","year":"2026","journal-title":"Comput. Vis. Media"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Avraham, D., Samuels, G., Jung, J.-H., Peli, E., and Yitzhaky, Y. (2022). Computational integral imaging based on a novel miniature camera array. Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP) (2022), paper 3Tu5A.2, Optica Publishing Group.","DOI":"10.1364\/3D.2022.3Tu5A.2"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1700003","DOI":"10.1002\/bies.201700003","article-title":"Phototoxicity in live fluorescence microscopy, and how to avoid it","volume":"39","author":"Icha","year":"2017","journal-title":"BioEssays"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gupta, M., Yin, Q., and Nayar, S.K. (2013, January 1\u20138). Structured Light in Sunlight. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.73"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pears, N., Liu, Y., and Bunting, P. (2012). Passive 3D Imaging. 3D Imaging, Analysis and Applications, Springer.","DOI":"10.1007\/978-1-4471-4063-4"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"32266","DOI":"10.1364\/OE.402193","article-title":"Roadmap on 3D integral imaging: Sensing, processing, and display","volume":"28","author":"Javidi","year":"2020","journal-title":"Opt. Express"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/JPROC.2006.870696","article-title":"Three-Dimensional Image Sensing, Visualization, and Processing Using Integral Imaging","volume":"94","author":"Stern","year":"2006","journal-title":"Proc. IEEE"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1364\/JOSAA.31.002312","article-title":"Human gesture recognition using three-dimensional integral imaging","volume":"31","author":"Traver","year":"2014","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_43","unstructured":"Ravi, M., Gabeur, V., Hu, Y.T., Hu, R., Ryall, C., Ma, T., Khedr, H., Radle, R., Rolland, C., and Gustafson, L. (2024). SAM 2: Segment Anything in Images and Videos. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., and Liao, H.Y. (2024). Yolov9: Learning what you want to learn using programmable gradient information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1364\/AO.56.002132","article-title":"Effects of elemental-images\u2019 quantity on 3D segmentation using computational integral-imaging","volume":"56","author":"Aloni","year":"2017","journal-title":"Appl. Opt."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T10:53:52Z","timestamp":1782903232000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,1]]},"references-count":45,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["s26134149"],"URL":"https:\/\/doi.org\/10.3390\/s26134149","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,1]]}}}