PyMeshLab
PyMeshLab is a Python library that interfaces to MeshLab, the popular open source application for editing and processing large 3D triangle meshes. Python bindings are generated using pybind11.
Documentation
You can find the official documentation here.
Install PyMeshLab
You can easily install PyMeshLab using pip:
pip3 install pymeshlab
Note about Conda
PyMeshLab is now available on Conda-Forge.
If you are in a Conda environment, we recommend installing PyMeshLab from Conda-Forge:
conda install -c conda-forge pymeshlab
See this discussion for more information.
Run PyMeshLab
After installing PyMeshLab through pip:
python
>>> import pymeshlab
>>> ms = pymeshlab.MeshSet()
You can load, save meshes and apply MeshLab filters:
ms.load_new_mesh('airplane.obj')
ms.generate_convex_hull()
ms.save_current_mesh('convex_hull.ply')
覆盖条件：invocation_mode=sdk
结构化配方尚未提供或不适用于本版本；旧用法文字保留，连接与执行结果未知。
稳定标识
cap-39652340b193af9209cdccea
名片版本
2
资料更新
2026-10-31T00:09:28.2282230+00:00
原作者 / 提供者
CNR-ISTI Visual Computing Lab / PyMeshLab
可用状态
未知；收录不代表正在接单或有实时库存。
本站收费
免费；第三方条件另行核对。
输入
获准处理的本地三角网格及明确的几何处理目标
输出
凸包网格及目标版本支持的导出文件
使用方式
类型
本机代码库
入口
未知
HTTP 方法
未知
文档中的本机命令（此页面不会执行）：
import pymeshlab
ms = pymeshlab.MeshSet()
ms.load_new_mesh("input.obj")
ms.generate_convex_hull()
ms.save_current_mesh("convex_hull.ply")
{
"url": "https://github.com/cnr-isti-vclab/PyMeshLab",
"final_url": "https://github.com/cnr-isti-vclab/PyMeshLab",
"status_code": 200,
"title": "GitHub - cnr-isti-vclab/PyMeshLab: The open source mesh processing python library \u00B7 GitHub",
"description": "The open source mesh processing python library. Contribute to cnr-isti-vclab/PyMeshLab development by creating an account on GitHub.",
"checked_at": "2026-10-20T12:20:51.7092241\u002B00:00",
"warnings": [
"\u6765\u6E90\u53EF\u8BFB\u53D6\u53EA\u8BC1\u660E\u672C\u6B21\u8D44\u6599\u8BFB\u53D6\uFF0C\u4E0D\u662F\u529F\u80FD\u3001\u8EAB\u4EFD\u6216\u957F\u671F\u53EF\u7528\u6027\u8BA4\u8BC1\u3002"
]
}
公开使用观察
暂无公开记录，不代表失败。
有依据的更正与异议
暂无公开记录，不代表失败。
补充观察或有依据的更正
通过同一公开投稿 API 或 MCP 工具提交。只发公开的必要事实，不上传私人任务、身份或凭据。提交者观察不增加商业调用和收益。