作为一种反映物体形貌的三维信息, 点云数据的原始数据量十分庞大, 直接对过多的数据进行操作会影响后续重建等工作。本文提出了一种新的点云特征提取自适应精简算法。首先对原始点云进行空间划分, 构建点的k邻域, 设置特征参数, 进行特征分析, 识别不同区域的信息和数据。然后针对平面数据预先进行边界的检测和提取, 对剩余部分进行精简。最后, 针对非平面区域, 先提取特征, 再根据曲率的不同进行不同程度的精简。办公室数据扫描实验结果表明, 处理大小为百万以内点的点云模型可以在几秒之内完成, 精简比能够达到90%以上, 与原始数据间的误差较小: 平面部分在精简前后平均偏差均在0.02 mm以内, 波动很小, 为0.005 7 mm; 非平面区域精简前后的平均偏差均在0.08 mm左右, 差值仅为0000 3 mm, 精简精度得以保证。因此, 利用提出的算法处理后的数据能更好地展示物体的形貌。

Point cloud data, as a kind of three-dimensional information reflecting the object shape, have quite a large amount of original data, so if directly operating on excessive data, it will affect subsequent work such as point clouds reconstruction, etc. This paper proposes a novel adaptive simplification algorithm for point cloud feature extraction. First, space should be divided with respect to the original point cloud, and then k neighborhood of the point should be built, and feature parameters should be set up, and then feature analysis should be conducted, and finally information and data of different parts should be identified. Then, for the planar data, the boundary is detected and extracted and the remaining parts are simplified. Finally, for the nonplanar data, the feature is extracted and then simplifications are implemented in varying degrees according to different curvatures. Experiments show that it takes no more than several seconds to process a point cloud model with almost a million points. Simplification proportion can reach above 90%, and the error corresponding to original data is smaller: the average deviation of the planar data is less than 0.02 mm before and after simplification, with a small fluctuation at 0.005 7 mm; the average deviation of the nonplanar data is likely to fluctuate around 0.08 mm and the difference is only 0.000 3 mm before and after simplification, guaranteeing the simplification accuracy. Therefore, the data processed by proposed algorithm can display the object shape better.