Numpy-stl库以numpy数组的形式加载stl文件。'Vectors'属性是Nx 3x 3 numpy数组。简单地说，这个数组是形状的面的集合。因此N代表面的数量，3是定义图形面的点数，最终尺寸表示3D空间中的点坐标。
在大多数库中，要绘制3D形状，您需要定义两件事-点的坐标和点的索引以绘制图形的面。
numpy-stl简化了过程，因为所有的点都是唯一的，并且是有序的。这意味着定义面的点的索引只是顶点数组中的索引。
另外值得一提的是，你需要定义的不是点本身，而是x，y，z分别。同样的计数面。
基于此，我们冷写这个。我选择plotly，因为它快速和互动

import plotly.graph_objects as go
from stl import mesh  # numpy-stl
import numpy as np
data = mesh.Mesh.from_file('fabric.stl')
points = data.vectors.reshape(-1,3)  # reshape Nx3x3 to N*3x3 to convert polygons to points
faces_index = np.arange(len(points))  # get indexes of points
fig = go.Figure(data=[
    go.Mesh3d(
        x = points[:,0],  # pass first column of points array
        y = points[:,1],  # .. second column
        z = points[:,2],  # .. third column
        # i, j and k give the vertices of triangles
        i = faces_index[0::3],  # indexes of the points. k::3 means pass every third element 
        j = faces_index[1::3],  # starting from k element
        k = faces_index[2::3],  # for example 2::3 in arange would be 2,5,8,...
        opacity = .9
    )
])
fig.show()

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显示：x1c 0d1x
图片来自默认的windows stl文件查看工具：

结果看起来是正确的。请忽略绘图顶部的蓝点和顶部环附近的孔，这是文件工件，并在两个变体中呈现。

由于顶点被多次使用，所以首先在向量数组中找到唯一的点可能会有所帮助。下面是一个使用pandas删除重复项并定义三角形和open3d进行可视化的示例。
df.drop_duplicates删除了重复的顶点，现在我们需要根据旧的顶点列表找到新的顶点列表的索引。我用pandas merge做到了这一点。

import open3d as o3d
import numpy as np
import pandas as pd
def extract_vertices_and_triangles(mesh_vertices):
    # Reshape the array to (n*3, 3)
    flat_vertices = mesh_vertices.reshape(-1, 3)
    # Create a DataFrame for easy manipulation
    df = pd.DataFrame(flat_vertices, columns=["x", "y", "z"])
    # Create a new DataFrame with unique vertices
    unique_df = df.drop_duplicates(subset=["x", "y", "z"]).reset_index(drop=True)
    # Create a mapping from old indices to unique indices
    merged_df = (
        df.reset_index()
        .merge(unique_df.reset_index(), on=["x", "y", "z"], how="right")
        .sort_values(by="index_x")
    )
    triangles = merged_df["index_y"].to_numpy()
    triangles = triangles.reshape(-1, 3)
    # Reshape the unique vertices to the original shape
    unique_vertices = unique_df[["x", "y", "z"]].values
    return unique_vertices, triangles
if __name__ == "__main__":
    armadillo_mesh = o3d.data.ArmadilloMesh()
    mesh = o3d.io.read_triangle_mesh(armadillo_mesh.path)
    mesh.compute_vertex_normals()
    o3d.visualization.draw_geometries([mesh])
    vertices = np.array(mesh.vertices)
    triangles = np.array(mesh.triangles)
    np_vertices = vertices[triangles]
    vert, triangle = extract_vertices_and_triangles(np_vertices)
    # Create Open3D TriangleMesh
    mesh = o3d.geometry.TriangleMesh()
    mesh.vertices = o3d.utility.Vector3dVector(vert)
    mesh.triangles = o3d.utility.Vector3iVector(triangle)
    mesh.compute_vertex_normals()
    # Visualize the extracted mesh
    o3d.visualization.draw_geometries([mesh])

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