Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs


Python/Pytorch implementation of the superpoint graph algorithm, for semantic segmentation of large point clouds.

Cut pursuit

The new code of cut pursuit for nonsmooth function will be uploaded in a few days!

L0-Cut pursuit


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.