yangyanli/PointCNN
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)
This project helps classify and segment 3D objects represented as point clouds, which are collections of data points in 3D space. You input raw point cloud data from sources like 3D scanners, and the system outputs either the object's category (e.g., 'chair', 'car') or labels for each point defining different parts of an object (e.g., 'armrest', 'wheel'). This is ideal for researchers or engineers working with 3D spatial data in fields like robotics, autonomous driving, or architectural modeling.
1,428 stars.
Use this if you need to accurately identify and categorize 3D objects or their components from raw point cloud data.
Not ideal if your data is not in a point cloud format or if you are looking for a pre-packaged application rather than a research framework.
Stars
1,428
Forks
364
Language
Python
License
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Category
Last pushed
Mar 12, 2026
Commits (30d)
0
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