LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning

[2604.16910] LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2604.16910 (cs) [Submitted on 18 Apr 2026 ( v1 ), last revised 15 Aug 2026 (this version, v3)] Title: LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning Authors: Yikun Wang , Yujie Wan , Wei Zuo , Shuai Wang , Yik-Chung Wu , Chengzhong Xu , Huseyin Arslan View a PDF of the paper titled LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning, by Yikun Wang and 6 other authors View PDF HTML (experimental) Abstract: Low-altitude Gaussian splatting (LAGS) facilitates 3D scene reconstruction by aggregating aerial images from distributed drones. However, as LAGS prioritizes maximizing reconstruction quality over communication throughput, existing low-altitude resource allocation schemes become inefficient. This inefficiency stems from their failure to account for image diversity introduced by varying viewpoints. To fill this gap, we propose a groupwise heterogeneous graph neural network (GW-HGNN) for LAGS resource allocation. GW-HGNN explicitly models the non-uniform contribution of different image groups to the reconstruction process, thus automatically balancing data fidelity and transmission cost. The key insight of GW-HGNN is to transform LAGS losses and communication constraints into graph learning costs for dual-level message passing. Experiments on real-world LAGS datasets demonstrate that GW-HGNN significantly outperforms state-of-the-art benchmarks across key rendering metrics, including PSNR, SSIM, and LPIPS. Furthermore, GW-HGNN reduces computational latency by approximately 100x compared to the widely-used MOSEK solver, achieving millisecond-level inference suitable for real-time deployment. Comments: 8 pages, 12 figures, 2 tables Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Robotics (cs.RO) Cite as: arXiv:2604.16910 [cs.CV] (or arXiv:2604.16910v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2604.16910 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yikun Wang [ view email ] [v1] Sat, 18 Apr 2026 08:41:50 UTC (2,020 KB) [v2] Sat, 25 Jul 2026 13:38:03 UTC (2,573 KB) [v3] Sat, 15 Aug 2026 06:36:22 UTC (3,395 KB) Full-text links: Access Paper: View a PDF of the paper titled LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning, by Yikun Wang and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-04 Change to browse by: cs cs.RO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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