A Fast Data Reduction Method for Massive Scattered Point Clouds Based on Slicing
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摘要: 提出基于切片的海量散乱点云快速压缩方法,对点云进行分层生成切片点云,对每层切片点云使用弦高差法筛选利于表现形状的重要点,实现快速压缩。通过实验讨论参数对压缩结果的影响,并给出最佳参数值选择依据。对本方法和传统方法的压缩效果进行对比,证实本方法在实现高效压缩的同时能保留大量的特征细节。Abstract: Abstract: This paper puts forward a high-efficiency data reduction method for massive scattered point clouds. The proposed method is based on slicing technology. Firstly，the point cloud is subdivided into several layers，then a reference plane is set for each layer，and the points set within each layer are projected to the relevant plane，thus slicing the point cloud for each layer. Second，points that are important for the shape expression for each sliced point cloud are extracted using a chordal deviation method. Two key parameter ，layer number and chordal deviation threshold are discussed，concluding is made that the chordal deviation threshold must be smaller than the minimum average chordal deviation for each slice. Reduction results for both the proposed method and the traditional methods are compared in an experiment. Results show that the proposed method achieves high-quality reduction results in an efficient manner with wet preserved features and details.
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Keywords:
- massive scattered point clouds /
- slicing /
- data reduction /
- feature preservation
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