All the code is available at the GitHub repository HERE. For this article, I have chosen 8 libraries that provide different levels of visualizations and a number of additional options - from built-in manual controls to a large number of lighting and animation options and raytracing visuals. Others like Simple-3dviz, Polyscope, and Pyrender are more directed towards creating fast and beautiful visualizations and animations. Some of these libraries like Open3D, Trimesh, and Vedo are extremely robust and contain many different functionalities for the analysis, generation, and manipulation of meshes and point clouds. In recent years more and more Python libraries have started to pop up to try to fill this void. In short - if you work in Linux and you are not afraid of C++, PCL is the way to go. Two main problems stop it from being perfect - the Python wrapper contains only a small subset of the functionality of the main PCL library and running it on Windows is an exercise in frustration, with multitudes of compilation errors, missing or broken functionality, and less than optimal tutorials. PCL is still one of the best libraries out there for 3D analysis and the fact that it is built in C++ guarantees that is versatile and responsive. This led to many people using the Point Cloud Library ( PCL) as a catch-all solution for the visualization and analysis of meshes and point clouds. The bad thing is that it does not support GPU hardware acceleration, meaning that once you pass a certain number of points or faces in 3D, things tend to become slow and unresponsive. The good thing about using it is that if you have a Python installation, you most probably have everything that you need to visualize data in 3D using Matplotlib. Visualizing weather (Temperature/Humidity) data for different months in 3D using PyVista | Image by the authorįor a long time, people needed to use Matplotlib to visualize 3D content in Python.
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