point cloud -> deep network -> classification / segmentation / super-resolution
traditional classification / segmentation:
projection onto 2D plane and use 2D classification / segmentation
unordered set
point(Vec3) -> feature vector (Vec5) -> normalize (end with the bound of the pointcloud)
N points:
segmentation:
feature from N points ->NxK classes of each point (each point will have a class)
classification:
feature from N points -> K x 1 vector (K classes)