Sapling-NeRF: 森林における苗木の地理的位置推定再構成による生態モニタリング
Sapling-NeRF: Geo-Localised Sapling Reconstruction in Forests for Ecological Monitoring
NeRF・LiDAR SLAM・GNSSを融合し、森林内の苗木を地理的位置を保ったまま高精度に3D再構成する手法を提案。幹の高さや枝分かれ、葉と木質の比率をTLSより正確に計測できる。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Miguel Ángel Muñoz-Bañón, Nived Chebrolu, Sruthi M. Krishna Moorthy, Yifu Tao, Fernando Torres, Roberto Salguero-Gómez, Maurice Fallon
分類: cs.RO, cs.CV
原文アブストラクト
Saplings are key indicators of forest regeneration and overall forest health. However, their fine-scale architectural traits are difficult to capture with existing 3D sensing methods, which make quantitative evaluation difficult. Terrestrial Laser Scanners (TLS), Mobile Laser Scanners (MLS), or traditional photogrammetry approaches poorly reconstruct thin branches, dense foliage, and lack the scale consistency needed for long-term monitoring. Implicit 3D reconstruction methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) are promising alternatives, but cannot recover the true scale of a scene and lack any means to be accurately geo-localised. In this paper, we present a pipeline which fuses NeRF, LiDAR SLAM, and GNSS to enable repeatable, geo-localised ecological monitoring of saplings. Our system proposes a three-level representation: (i) coarse Earth-frame localisation using GNSS, (ii) LiDAR-based SLAM for centimetre-accurate localisation and reconstruction, and (iii) NeRF-derived object-centric dense reconstruction of individual saplings. This approach enables repeatable quantitative evaluation and long-term monitoring of sapling traits. Our experiments in forest plots in Wytham Woods (Oxford, UK) and Evo (Finland) show that stem height, branching patterns, and leaf-to-wood ratios can be captured with increased accuracy as compared to TLS. We demonstrate that accurate stem skeletons and leaf distributions can be measured for saplings with heights between 0.5m and 2m in situ, giving ecologists access to richer structural and quantitative data for analysing forest dynamics.
