Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
Python/Pytorch implementation of the superpoint graph algorithm for semantic segmentation of large point clouds.
C++ implementation of the cut pursuit algorithm for non-smooth fucntions regularized by the graph total variation, with MATLAB, Octave and python wrappers.
Implementation of the L0-cut pursuit algorithms to compute piecewise constant approximation of a function defined on a graph when regularized by the weight of the cuts between adjacent constant components.
Regularization and segmentation framework for point clouds classification (MATLAB)
Provide a benchmark of all methods presented in the paper `A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds`. Only require a ply file and a probabilistic classification to smooth.
An interface for fast partition of point clouds into geometrically simple shapes. I does not provide a one-to-tone instance segmentation of objects, but a sursegmentation in which the clusters are generally semantically homogeneous. As used in the SuperPoint Graph paper.