Code: (pytorch)

Problem Definition

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



Key Points

  • 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:

ShapeNet part dataset: