{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T22:08:04Z","timestamp":1764194884970,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42071340","2211000211000-01"],"award-info":[{"award-number":["42071340","2211000211000-01"]}]},{"name":"Program of Song Shan Laboratory (included in the management of Major Science and Technology of Henan Province)","award":["42071340","2211000211000-01"],"award-info":[{"award-number":["42071340","2211000211000-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In traditional 3D reconstruction using UAV images, only radiance information, which is treated as a geometric constraint, is used in feature matching, allowing for the restoration of the scene\u2019s structure. After introducing radiance supervision, NeRF can adjust the geometry in the fixed-ray direction, resulting in a smaller search space and higher robustness. Considering the lack of NeRF construction methods for aerial scenarios, we propose a new NeRF point sampling method, which is generated using a UAV imaging model, compatible with a global geographic coordinate system, and suitable for a UAV view. We found that NeRF is optimized entirely based on the radiance while ignoring the direct geometry constraint. Therefore, we designed a radiance correction strategy that considers the incidence angle. Our method can complete point sampling in a UAV imaging scene, as well as simultaneously perform digital surface model construction and ground radiance information recovery. When tested on self-acquired datasets, the NeRF variant proposed in this paper achieved better reconstruction accuracy than the original NeRF-based methods. It also reached a level of precision comparable to that of traditional photogrammetry methods, and it is capable of outputting a surface albedo that includes shadow information.<\/jats:p>","DOI":"10.3390\/rs16224168","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T04:02:54Z","timestamp":1731038574000},"page":"4168","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Unmanned Aerial Vehicle-Neural Radiance Field (UAV-NeRF): Learning Multiview Drone Three-Dimensional Reconstruction with Neural Radiance Field"],"prefix":"10.3390","volume":"16","author":[{"given":"Li","family":"Li","sequence":"first","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Yongsheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8459-080X","authenticated-orcid":false,"given":"Zhipeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-5417","authenticated-orcid":false,"given":"Ziquan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Han","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"ref_1","unstructured":"Jiang, Y., Li, X., Zhu, G., Li, H., Deng, J., and Shi, Q. (2023). 6G Non-Terrestrial networks enabled low-altitude economy: Opportunities and challenges. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1007\/s11119-020-09777-5","article-title":"Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery","volume":"22","author":"Osco","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.cie.2018.05.013","article-title":"Persistent UAV delivery logistics: MILP formulation and efficient heuristic","volume":"120","author":"Song","year":"2018","journal-title":"Comput. Ind. Eng."},{"key":"ref_4","first-page":"1708","article-title":"Accuracy assessment of low cost UAV based city modelling for urban planning","volume":"25","author":"Erenoglu","year":"2018","journal-title":"Teh. Vjesn."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1002\/rob.22075","article-title":"Emerging UAV technology for disaster detection, mitigation, response, and preparedness","volume":"39","author":"Khan","year":"2022","journal-title":"J. Field Robot."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yao, Y., Luo, Z., Li, S., Fang, T., and Quan, L. (2018, January 8\u201314). Mvsnet: Depth inference for unstructured multi-view stereo. Proceedings of the European Conference on Computer Cision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_47"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1145\/3503250","article-title":"Nerf: Representing scenes as neural radiance fields for view synthesis","volume":"65","author":"Mildenhall","year":"2021","journal-title":"Commun. ACM"},{"key":"ref_8","first-page":"441","article-title":"Light field rendering","volume":"Volume 2","author":"Levoy","year":"2023","journal-title":"Seminal Graphics Papers: Pushing the Boundaries"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Derksen, D., and Izzo, D. (2021, January 20\u201325). Shadow Neural Radiance Fields for Multi-View Satellite Photogrammetry. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00126"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mar\u00ed, R., Facciolo, G., and Ehret, T. (2022, January 19\u201320). Sat-NeRF: Learning Multi-View Satellite Photogrammetry with Transient Objects and Shadow Modeling Using RPC Cameras. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00137"},{"key":"ref_11","unstructured":"Achlioptas, P., Diamanti, O., Mitliagkas, I., and Guibas, L. (2018, January 10\u201315). Learning representations and generative models for 3d point clouds. Proceedings of the International Conference on Machine Learning, PMLR, Stockholm, Sweden."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., and Guibas, L.J. (2016, January 27\u201330). Volumetric and multi-view cnns for object classification on 3d data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.609"},{"key":"ref_13","unstructured":"Kazhdan, M., Bolitho, M., and Hoppe, H. (2006, January 26\u201328). Poisson surface reconstruction. Proceedings of the Fourth Eurographics Symposium on Geometry Processing, Sardinia, Italy."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, Z., and Zhang, H. (2019, January 15\u201320). Learning implicit fields for generative shape modeling. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00609"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1111\/cgf.14340","article-title":"DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks","volume":"Volume 40","author":"Neff","year":"2021","journal-title":"Computer Graphics Forum"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3228927","article-title":"Remote sensing novel view synthesis with implicit multiplane representations","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., and Frahm, J.M. (2016, January 27\u201330). Structure-from-motion revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., and Quan, L. (2019, January 15\u201320). Recurrent mvsnet for high-resolution multi-view stereo depth inference. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00567"},{"key":"ref_19","unstructured":"Chen, R., Han, S., Xu, J., and Su, H. (November, January 27). Point-based multi-view stereo network. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gu, X., Fan, Z., Zhu, S., Dai, Z., Tan, F., and Tan, P. (2020, January 13\u201319). Cascade cost volume for high-resolution multi-view stereo and stereo matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00257"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yu, Z., and Gao, S. (2020, January 13\u201319). Fast-mvsnet: Sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00202"},{"key":"ref_22","unstructured":"Zhang, K., Riegler, G., Snavely, N., and Koltun, V. (2020). NeRF++: Analyzing and Improving Neural Radiance Fields. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.eja.2014.01.004","article-title":"Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods","volume":"55","author":"Angileri","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"229","DOI":"10.5194\/isprs-archives-XLII-2-W8-229-2017","article-title":"Preliminary tests of a new low-cost photogrammetric system","volume":"42","author":"Santise","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4168\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:28:40Z","timestamp":1760113720000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4168"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,8]]},"references-count":24,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224168"],"URL":"https:\/\/doi.org\/10.3390\/rs16224168","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,11,8]]}}}