41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
import open3d as o3d
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import os
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# 指定根目录路径
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root_path = "data/dynerf/sear_steak/"
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# 文件名
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input_file = "points3D.ply"
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output_file = "points3d_filtered.ply"
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# 读取点云数据
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point_cloud_before = o3d.io.read_point_cloud(os.path.join(root_path, input_file))
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# 计算过滤前的点的数量
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num_points_before = len(point_cloud_before.points)
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# 计算过滤前的点云的边界框大小
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bbox_before = point_cloud_before.get_axis_aligned_bounding_box()
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bbox_size_before = bbox_before.get_max_bound() - bbox_before.get_min_bound()
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# 进行离群点滤波
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cl, ind = point_cloud_before.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)
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# 创建一个新的点云对象，包含滤波后的点
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filtered_point_cloud = point_cloud_before.select_by_index(ind)
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# 保存滤波后的点云到新文件
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o3d.io.write_point_cloud(os.path.join(root_path, output_file), filtered_point_cloud)
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# 计算过滤后的点的数量
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num_points_after = len(filtered_point_cloud.points)
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# 计算边界框的大小
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bbox = filtered_point_cloud.get_axis_aligned_bounding_box()
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bbox_size = bbox.get_max_bound() - bbox.get_min_bound()
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print(f"过滤前的点数: {num_points_before}")
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print(f"过滤前的点云边界框大小: {bbox_size_before}")
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print(f"过滤后的点数: {num_points_after}")
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print(f"过滤后的点云边界框大小: {bbox_size}")
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print(f"离群点过滤完成，结果已保存到 {output_file}")
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