点云滤波是点云数据处理、分析和应用的必经环节，为突破滤波方法在复杂地形区域滤波精度低、鲁棒性差以及需要过多参数设置的瓶颈，本项目拟基于Snake模型进行多基元融合的滤波方法研究。拟采用多基元融合的滤波策略，可以有效避免单一基元滤波法鲁棒性差的缺陷，充分利用不同基元提供的特征信息，提高滤波方法对复杂地形区域的适应性。主要构建Snake模型，通过设置具有地面点吸附力的目标能量函数，使得模型能够兼顾点云的局部尺度信息与全局尺度信息，充分挖掘点云数据本身的自动化潜力。依据该模型将滤波阈值表示为地形坡度变化的线性函数，实现滤波阈值的自适应更新，避免单一阈值滤波精度差而多阈值需要过多参数设置的缺陷。此项研究有望获取在复杂地形区域具有较高滤波精度、鲁棒性以及自动化程度的滤波方法，为建筑物三维建模、地表覆盖分类、生物量估算等点云后处理应用提供基础支持，研究成果具有广泛的应用前景。

Point cloud filtering is a necessary step in the point cloud processing, analysis and applications. To break through the bottlenecks of the existing filtering methods, including low filtering accuracy in complex terrain environments, poor robustness, and too many parameter settings, this project will focus the research on fusion of multiple primitives for point cloud filtering based on the Snake model. Multiple primitives fusion filtering strategy will be adopted to avoid the drawback of poor robustness of filtering methods based on single primitive. In so doing, the adaptability of the filtering method to the complex terrain environments can be improved by making full use of the characteristic information provided by different primitives. The main contribution of the project is to develop the Snake model. By setting the target energy function with adsorption force to the ground points, the model can take both the local scale information and the global scale information of point cloud into account, which can fully exploit the automation potential of point cloud data itself. The filtering thresholds can be expressed as a linear function of terrain slope changes. In this way, the adaptive updating of filtering thresholds can be implemented so as to avoid the drawbacks of low filtering accuracy with single threshold and too many parameter settings involved for multiple thresholds. This project is expected to develop the filtering method with higher filtering accuracy, robustness, and automation degree in complicated terrain areas, and provide fundamental technical supports for the post-processing application of point clouds, such as three-dimensional building modeling, surface coverage classification, biomass estimation, etc. The research results cover wide application prospects.