charlesq34/pointnet2
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
This project helps engineers, researchers, or anyone working with 3D sensor data to automatically identify and categorize objects or specific parts within complex 3D environments. It takes raw 3D point cloud data, like that from LiDAR scanners or depth cameras, and outputs classifications of entire objects (e.g., 'chair', 'car') or segmentations of their individual components (e.g., 'chair leg', 'car wheel'). This is useful for tasks like robotic vision, autonomous navigation, or quality inspection.
3,617 stars. No commits in the last 6 months.
Use this if you need to perform accurate classification or detailed part segmentation on 3D point cloud data, especially when dealing with varying densities in your scans.
Not ideal if your primary data is 2D images or if you require an off-the-shelf solution without custom model training.
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Python
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Aug 26, 2022
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