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中山大学地理科学与规划学院,广东 广州 510006
Received:05 February 2025,
Revised:2025-04-22,
Accepted:29 April 2025,
Published Online:19 June 2025,
Published:25 September 2025
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刘原池,李文楷,刘红良等.基于旋转薄片体素化森林三维重构方法[J].中山大学学报(自然科学版)(中英文),2025,64(05):84-99.
LIU Yuanchi,LI Wenkai,LIU Hongliang,et al.Three-dimensional forest reconstruction using rotational slice-based voxelization[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2025,64(05):84-99.
刘原池,李文楷,刘红良等.基于旋转薄片体素化森林三维重构方法[J].中山大学学报(自然科学版)(中英文),2025,64(05):84-99. DOI: 10.13471/j.cnki.acta.snus.ZR20250023.
LIU Yuanchi,LI Wenkai,LIU Hongliang,et al.Three-dimensional forest reconstruction using rotational slice-based voxelization[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2025,64(05):84-99. DOI: 10.13471/j.cnki.acta.snus.ZR20250023.
森林三维场景的模拟有助于在结构尺度上研究森林生态系统结构与生态系统多样性间的联系。激光雷达点云数据的立方体体素化是一种常用的森林三维重构方法,但存在易高估叶片面积等缺点。 鉴于此,提出一种改进的薄片化旋转化体素方法,并与传统的立方体体素化方法进行对比研究,探讨二者模拟森林三维结构的精度差异。 研究使用虚拟和部分真实的森林场景数据,借助HELIOS++(Heidelberg LiDAR operations simulator)、VBRT(voxel-based radiative transfer)和PBRT(physically based ray tracer)模型,模拟机载激光雷达点云数据及多光谱影像。 结果表明:与立方体体素化相比,薄片化及随机旋转后的树叶模型可显著改善树木冠层的真实感,且在点云数据高度分布、冠层覆盖度等核心指标上与基准数据表现出更高的相关度。 研究提出的改进体素化方法优化了虚拟遥感数据的视觉效果和准确性,有助于森林精细化三维重构,在森林辐射传输研究方面具有良好的应用潜能。
Simulating realistic three-dimensional (3D) forest scenes is key to studying the relationship between forest ecosystem structure and biodiversity at the structural scale. Light detection and ranging (LiDAR) point cloud voxelization is widely used for 3D forest reconstruction, but it usually overestimates the leaf areas of canopies. Here we propose an improved rotational slice-based voxelization method for 3D forest reconstruction. Using two synthetic forest scenes, we compared different 3D reconstruction methods by simulating airborne laser scanning data and multi-spectral imagery based on HELIOS++ (Heidelberg LiDAR operations simulator), VBRT (voxel-based radiative transfer), and PBRT (physically based ray tracer), respectively. Experimental results show that the slice-based and randomly rotated leaf models significantly improve canopy realism and produce a higher correlation with benchmark results for key metrics, such as point cloud height distribution and canopy coverage, compared with the traditional cubic voxelization. The new voxelization method proposed in this study enhances the visual effects and accuracy of simulated remote sensing data, contributing to more accurate and realistic 3D forest reconstruction and demonstrating good potential in forest radiative transfer modeling.
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