{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T09:20:17Z","timestamp":1778145617800,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Laboratory of Equipment State Sensing and Smart Support","award":["6142003202405"],"award-info":[{"award-number":["6142003202405"]}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"crossref","award":["2023JJ40037"],"award-info":[{"award-number":["2023JJ40037"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Sparse local feature matching has served as the cornerstone of numerous visual geometry tasks and attracted extensive attention. Although significant progress has been made in this area, improving the discriminative power of descriptors remains a key challenge. As far as we know, existing sparse feature matching methods only predict a single descriptor map for keypoints, which might restrict their potential in solving complex scenarios. This issue is particularly pronounced in real-time applications where most methods only learn descriptor maps at a reduced spatial resolution compared to the input image. Consequently, they require interpolating from the low resolution map for obtaining per-keypoint descriptors, which will introduce background contamination and reduce the discriminability of final descriptors. To address these issues, we propose an efficient novel complementary local feature description model. Specifically, the model simultaneously learns two descriptor maps using different loss functions within a single Convolutional Neural Network (CNN). An orthogonal loss is introduced to effectively coordinate the learning of the two branches, aiming to obtain decoupled and complementary descriptors. Extensive experiments across various visual geometry tasks, such as homography estimation, indoor and outdoor pose estimation, as well as visual localization, have demonstrated the superior performance of the proposed method.<\/jats:p>","DOI":"10.3390\/jimaging12050201","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T13:23:37Z","timestamp":1777987417000},"page":"201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Single Descriptors: Complementary Feature Learning for Image Matching"],"prefix":"10.3390","volume":"12","author":[{"given":"Xianguo","family":"Yu","sequence":"first","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"},{"name":"National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulong","family":"Feng","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering, Changsha University of Science and Technology, Changsha 410076, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering, Changsha University of Science and Technology, Changsha 410076, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.isprsjprs.2025.04.002","article-title":"SfM on-the-fly: A robust near real-time SfM for spatiotemporally disordered high-resolution imagery from multiple agents","volume":"224","author":"Zhan","year":"2025","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2026","DOI":"10.1109\/TCSVT.2024.3491506","article-title":"PAS-SLAM: A Visual SLAM System for Planar-Ambiguous Scenes","volume":"35","author":"Hu","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7426","DOI":"10.1109\/TITS.2025.3556928","article-title":"Semantic Visual Simultaneous Localization and Mapping: A Survey","volume":"26","author":"Chen","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1109\/LRA.2024.3518071","article-title":"Multi Map Visual Localization for Unmanned Aerial Vehicles","volume":"10","author":"Torresen","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201322). SuperPoint: Self-Supervised Interest Point Detection and Description. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3101","DOI":"10.1109\/TMM.2022.3155927","article-title":"ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction","volume":"25","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Edstedt, J., Sun, Q., B\u00f6kman, G., Wadenb\u00e4ck, M., and Felsberg, M. (2024, January 16\u201324). RoMa: Robust Dense Feature Matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01871"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Potje, G., Cadar, F., Araujo, A., Martins, R., and Nascimento, E.R. (2024, January 16\u201324). XFeat: Accelerated Features for Lightweight Image Matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00259"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., Rocco, I., Pajdla, T., Pollefeys, M., Sivic, J., Torii, A., and Sattler, T. (2019, January 15\u201320). D2-Net: A Trainable CNN for Joint Description and Detection of Local Features. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00828"},{"key":"ref_10","unstructured":"Revaud, J., De Souza, C., Humenberger, M., and Weinzaepfel, P. (2019, January 8\u201314). R2D2: Reliable and Repeatable Detector and Descriptor. Proceedings of the Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3989","DOI":"10.1109\/TMM.2022.3169331","article-title":"CNDesc: Cross Normalization for Local Descriptors Learning","volume":"25","author":"Wang","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., and Nie\u00dfner, M. (2017, January 21\u201326). ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.261"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, Z., and Snavely, N. (2018, January 18\u201323). MegaDepth: Learning Single-View Depth Prediction from Internet Photos. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00218"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shen, T., Luo, Z., Zhou, L., Zhang, R., Zhu, S., Fang, T., and Quan, L. (2018). Matchable Image Retrieval by Learning from Surface Reconstruction. Proceedings of the Asian Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-20887-5_26"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s11263-020-01399-8","article-title":"Reference Pose Generation for Long-Term Visual Localization via Learned Features and View Synthesis","volume":"129","author":"Zhang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Balntas, V., Lenc, K., Vedaldi, A., and Mikolajczyk, K. (2017, January 21\u201326). HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.410"},{"key":"ref_17","unstructured":"Barroso-Laguna, A., and Mikolajczyk, K. (2023, January 1\u20136). Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters Revisited. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhou, X., Hariharan, B., and Snavely, N. (2020). Learning Feature Descriptors Using Camera Pose Supervision. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58452-8_44"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Verdie, Y., Yi, K., Fua, P., and Lepetit, V. (2015, January 7\u201312). TILDE: A Temporally Invariant Learned Detector. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299165"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Savinov, N., Seki, A., Ladicky, L., Sattler, T., and Pollefeys, M. (2017, January 21\u201326). Quad-Networks: Unsupervised Learning to Rank for Interest Point Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.418"},{"key":"ref_22","unstructured":"Han, X., Leung, T., Jia, Y., Sukthankar, R., and Berg, A.C. (2015, January 7\u201312). MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tian, Y., Fan, B., and Wu, F. (2017, January 21\u201326). L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.649"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shen, X., Wang, C., Li, X., Yu, Z., Li, J., Wen, C., Cheng, M., and He, Z. (2019, January 16\u201320). RF-Net: An End-To-End Image Matching Network Based on Receptive Field. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00832"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yi, K.M., Trulls, E., Lepetit, V., and Fua, P. (2016). LIFT: Learned Invariant Feature Transform. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46466-4_28"},{"key":"ref_26","first-page":"4829","article-title":"Working Hard to Know Your Neighbor\u2019s Margins: Local Descriptor Learning Loss","volume":"30","author":"Mishchuk","year":"2017","journal-title":"NeurIPS"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tian, Y., Yu, X., Fan, B., Wu, F., Heijnen, H., and Balntas, V. (2019, January 15\u201320). SOSNet: Second Order Similarity Regularization for Local Descriptor Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01127"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xue, F., Budvytis, I., and Cipolla, R. (2023, January 18\u201322). SFD2: Semantic-Guided Feature Detection and Description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00504"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Luo, Z., Zhou, L., Bai, X., Chen, H., Zhang, J., Yao, Y., Li, S., Fang, T., and Quan, L. (2020, January 14\u201319). ASLFeat: Learning Local Features of Accurate Shape and Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00662"},{"key":"ref_30","unstructured":"Barroso-Laguna, A., Verdie, Y., Busam, B., and Mikolajczyk, K. (December, January 30). HDD-Net: Hybrid Detector Descriptor with Mutual Interactive Learning. Proceedings of the Asian Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_31","first-page":"14254","article-title":"DISK: Learning Local Features with Policy Gradient","volume":"33","author":"Tyszkiewicz","year":"2020","journal-title":"NeurIPS"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bhowmik, A., Gumhold, S., Rother, C., and Brachmann, E. (2020, January 14\u201319). Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00500"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, K., Wang, L., Liu, L., Ran, Q., Xu, K., and Guo, Y. (2022, January 18\u201324). Decoupling Makes Weakly Supervised Local Feature Better. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01538"},{"key":"ref_34","first-page":"5014016","article-title":"ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation","volume":"72","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gleize, P., Wang, W., and Feiszli, M. (2023). SiLK: Simple Learned Keypoints. arXiv.","DOI":"10.1109\/ICCV51070.2023.02056"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Potje, G., Cadar, F., Araujo, A., Martins, R., and Nascimento, E.R. (2023, January 18\u201322). Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00132"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft COCO: Common Objects in Context. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_38","unstructured":"Melekhov, I., Brostow, G.J., Kannala, J., and Turmukhambetov, D. (2020). Image Stylization for Robust Features. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Sattler, T., and Leal-Taixe, L. (2021, January 19\u201325). Patch2Pix: Epipolar-Guided Pixel-Level Correspondences. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.00464"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sun, J., Shen, Z., Wang, Y., Bao, H., and Zhou, X. (2021, January 19\u201325). LoFTR: Detector-Free Local Feature Matching with Transformers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., DeTone, D., Malisiewicz, T., and Rabinovich, A. (2020, January 14\u201319). SuperGlue: Learning Feature Matching with Graph Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00499"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/201\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T09:06:13Z","timestamp":1778144773000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,5]]},"references-count":41,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jimaging12050201"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12050201","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,5]]}}}