[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2781":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":11,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":16,"score_detail":17,"sources":25,"tags":27,"view_count":33,"doi":34,"paper":35,"created_at":56},2781,"Evaluating Mesh Reconstruction Methods for Crop Phenotyping","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.16926","Phenotyping an agricultural crop is crucial for studying its entire life cycle, as it provides vital insights to improve yield and, ultimately, food production. Doing the same for crops grown on remote sites is a challenge for the specialists who cannot be available on-site. 3D reconstruction techniques offer a promising solution to this problem by enabling crop digitization, allowing specialists to access the resulting 3D crop models from anywhere at any time. In this work, we evaluate recent 3D reconstruction pipelines for crop phenotyping. We focus on 7 mesh reconstruction pipelines and measure the fidelity and consistency of their outputs qualitatively and quantitatively. Our results suggest that the meshes produced by the GGGS, PGSR, and 2DGS are preferable to the other pipelines, owing to their quantitative metrics and visually pleasing outputs. The GGGS pipeline is better than the second-best pipeline (2DGS) by about 27\\% on the radar chart with 5 dimensions, namely, User ratings, Chamfer distance, LPIPS, PSNR, and SSIM.","对农作物进行表型分析对于研究其整个生命周期至关重要，因为它为提高产量并最终提升粮食生产提供了关键见解。对于生长在偏远地区的作物而言，由于专家无法亲临现场，开展同样的表型分析是一项挑战。三维重建技术通过实现作物数字化，使专家能够随时随地访问生成的作物三维模型，从而为这一问题提供了有前景的解决方案。在本研究中，我们评估了近期用于作物表型分析的三维重建流程。我们聚焦于7种网格重建流程，并对其输出的保真度和一致性进行了定性和定量评估。结果表明，GGGS、PGSR和2DGS生成的网格在定量指标和视觉输出方面优于其他流程。在包含5个维度（用户评分、倒角距离、LPIPS、PSNR和SSIM）的雷达图上，GGGS流程比排名第二的2DGS流程高出约27%。",null,"arXiv (Cornell University)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.16926","2026-09-15T00:00:00Z","论文",10,false,74,{"impact":18,"substance":19,"depth":20,"authority":21,"freshness":22,"relevant":23,"comment":24},16,20,17,13,8,1,"系统评测7种网格重建流程用于作物表型数字化，结论明确、指标可量化，对远程作物表型与三维数字化研究有实质参考价值。",[26],{"name":10,"url":11},[28,29,30,31,32],"智慧农业","农业人工智能","遥感","作物表型","三维重建",0,"10.48550\u002Farxiv.2609.16926",{"doi":34,"openalex_id":36,"authors":37,"venue":10,"cited_by_count":33,"oa_url":48,"card":49,"direction":53,"ingested_from":55},"W7213397688",[38,41,43,46],{"name":39,"orcid":40},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":42,"orcid":9},"Theo Morales",{"name":44,"orcid":45},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":47,"orcid":9},"Mukesh Saini","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":50,"method":51,"finding":52,"direction":53,"opportunity":54},"评估7种网格重建流程在作物表型三维数字化中的保真度与一致性。","对比7种3D重建流程，用Chamfer距离、LPIPS、PSNR、SSIM及用户","GGGS、PGSR和2DGS输出更优，GGGS在五维雷达图上比2DGS高约27%。","农业遥感与作物表型","可探索轻量化、田间实时三维重建，并建立作物表型专用网格质量评价标准。","openalex","2026-09-17T23:30:27.096274Z"]