pointednet
PointNet is a neural network architecture designed for processing point cloud data. Point clouds are a collection of data points in a 3D space, often representing the shape of an object or environment. Traditional convolutional neural networks (CNNs) are not well-suited for point clouds because the data is unordered and irregular, lacking the grid-like structure that CNNs rely on. PointNet addresses this by treating the input as a set of points.
The architecture employs symmetric functions, such as max pooling, to achieve permutation invariance. This means that
Key components of PointNet include shared Multi-Layer Perceptrons (MLPs) that process each point independently to learn