对比NVS与摄影测量法重建森林树木，发现新视角合成更高效但精度各有优劣。
Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and extraction of individual tree parameters
- 用NeRF和3DGS进行少样本3D森林重建，提升效率
- NeRF在树冠重建中表现最好，但地面误差大；3DGS树干点云稀疏影响胸径精度
- 摄影测量法仍最准于胸径提取，适合高精度林分评估
精准高效的树木三维重建对森林资源评估与管理至关重要。近景摄影测量（CRP）常用于森林场景重建，但存在效率低、质量差的问题。近年来，新视角合成（NVS）技术如神经辐射场（NeRF）和3D高斯泼溅（3DGS）在有限图像下展现出植物三维重建潜力。然而，现有研究多集中于果园小植株或单棵树，其在复杂大尺度林分中的应用尚不明确。本研究采集了不同复杂度林地的连续影像，分别使用NeRF和3DGS进行密集重建，并与摄影测量及激光扫描结果对比。结果显示，NVS方法显著提升重建效率。摄影测量在复杂林分中表现不佳，导致点云噪声过大且出现重复树干等错误。NeRF虽能较好重建树冠，但在视图受限的地面区域易出错；3DGS生成的点云较稀疏，尤其在树干区域，影响胸径（DBH）测量精度。三者均可提取树高信息，其中NeRF准确度最高；但摄影测量在DBH提取上仍最优。这些发现表明，NVS技术在复杂林分三维重建中具有重要潜力，可为林分资源清查与可视化提供有力支持。
原文摘要 · Abstract (English)
Accurate and efficient 3D reconstruction of trees is crucial for forest resource assessments and management. Close-Range Photogrammetry (CRP) is commonly used for reconstructing forest scenes but faces challenges like low efficiency and poor quality. Recently, Novel View Synthesis (NVS) technologies, including Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have shown promise for 3D plant reconstruction with limited images. However, existing research mainly focuses on small plants in orchards or individual trees, leaving uncertainty regarding their application in larger, complex forest stands. In this study, we collected sequential images of forest plots with varying complexity and performed dense reconstruction using NeRF and 3DGS. The resulting point clouds were compared with those from photogrammetry and laser scanning. Results indicate that NVS methods significantly enhance reconstruction efficiency. Photogrammetry struggles with complex stands, leading to point clouds with excessive canopy noise and incorrectly reconstructed trees, such as duplicated trunks. NeRF, while better for canopy regions, may produce errors in ground areas with limited views. The 3DGS method generates sparser point clouds, particularly in trunk areas, affecting diameter at breast height (DBH) accuracy. All three methods can extract tree height information, with NeRF yielding the highest accuracy; however, photogrammetry remains superior for DBH accuracy. These findings suggest that NVS methods have significant potential for 3D reconstruction of forest stands, offering valuable support for complex forest resource inventory and visualization tasks.
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