Code: https://github.com/fxia22/pointnet.pytorch (pytorch)
Like CNNs, PointNets learn a global feature vector which can be used for tasks such as classification, segmentation, point normal estimation.
Input: Point Cloud
Output: Global feature vector for each point
- Each point is trained on multi layered perceptron(MLP) separately (with shared weights across points) and then the point is projected into 1024 dimension space through a series of transformations.
- This feature vector is used in different subnetworks for different tasks such as classification, segmentation etc.
ModelNet 40: http://modelnet.cs.princeton.edu/
ShapeNet part dataset: http://3dshapenets.cs.princeton.edu/