GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale Robots View Synthesis

GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale Robots View Synthesis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Journal of Field Robotics This is a preprint and has not been peer reviewed. Data may be preliminary. 4 February 2025 V1 Latest version Share on GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale Robots View Synthesis Authors : Zhiliu Yang 0000-0002-1009-9249 [email protected] , Hanyue Zhang , Xinhe Zuo , Yuxin Tong , Ying Long , and Chen Liu Authors Info & Affiliations https://doi.org/10.22541/au.173866895.50173418/v1 Published Journal of Field Robotics Version of record Peer review timeline 432 views 218 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the rendering deficiency and scalability challenges faced by existing embodied AI tasks. For tackling the scalability issue, we split the large scene into multiple cells, and the candidate point-cloud and camera views of each cell are correlated through a visibility-based camera selection and a progressive point-cloud extension. To reinforce the rendering quality, three highlighted improvements are made in comparison with vanilla 3DGS, which are a strategy of the ray-Gaussian intersection and the novel Gaussians density control for learning efficiency, an appearance decoupling module based on ConvKAN network to solve uneven lighting conditions in large-scale scenes, and a refined final loss with the color loss, the depth distortion loss, and the normal consistency loss. Finally, the seamless stitching procedure is executed to merge the individual Gaussian radiance field for novel view synthesis across different cells. Evaluation of Mill19, Urban3D, and MatrixCity datasets shows that our method consistently generates more high-fidelity rendering results than state-of-the-art methods of large-scale scene reconstruction. We further validate the generalizability of the proposed approach by rendering on self-collected video clips recorded by a commercial drone. Supplementary Material File (_jfr2025__garfield___reinforced_gaussian_radiance_fields_for_large_scale_robots view synthesisv10.pdf) Download 39.32 MB Information & Authors Information Version history V1 Version 1 04 February 2025 Peer review timeline Published Journal of Field Robotics Version of Record 26 Jun 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Journal of Field Robotics Keywords 3d robotic mapping computer vision localization optimization uavs Authors Affiliations Zhiliu Yang 0000-0002-1009-9249 [email protected] Yunnan University View all articles by this author Hanyue Zhang Yunnan University View all articles by this author Xinhe Zuo Yunnan University View all articles by this author Yuxin Tong Yunnan University View all articles by this author Ying Long Yunnan University View all articles by this author Chen Liu Clarkson University View all articles by this author Metrics & Citations Metrics Article Usage 432 views 218 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Zhiliu Yang, Hanyue Zhang, Xinhe Zuo, et al. GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale Robots View Synthesis. Authorea . 04 February 2025. DOI: https://doi.org/10.22541/au.173866895.50173418/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Haitao Luo, Jinming Zhang, Xiongfei Liu, Biqing Li, Jiaen Zhao, Lili Zhang, Junyi Liu, Shape-Optimized Gaussian Splatting for UAV Reconstruction with Manhattan Constraints, Electronics, 15 , 8, (1647), (2026). https://doi.org/10.3390/electronics15081647 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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