WME

An efficient algorithm to estimate the Weingarten map for submanifold point clouds

This readme page contains a basic documentation for the WME matlab code repository. We duplicate some information that can be found by help command in matlab. To quickly look at examples in matlab, first run

addPathToMatlab

to add all WME files to matlab path. Then use

help WME_function_name

to see the documentation for this function.

We also published tutorials for all WME functions. A complete list can be found in the following

WME_data

• WME_torus: generate random points from 2-dimensional torus
• WME_ellipsoid: generate random points from ellipsoids in various dimension (max dimension is 4)
• WME_read_data: read point clouds in various data type (support .mat, .txt, .ply)

WME_main

• WME_tangent_spaces: Estimate tangent/normal spaces for submanifold point cloud data using local PCA
• WME_orient_nom: Orient the normal vectors to get a consistent global normal vector field on manifold
• WME_wgtmap: Estimate the Weingarten map for submanifold point cloud data
• WME_mean_curv: Estimate the mean vector field on submanifold point cloud data

WME_draw

• WME_draw_points: scatter point clouds
• WME_draw_planes: draw tangent planes for point clouds
• WME_draw_normals: draw normal vectors on point clouds

Cite

Please cite the reference:

Yueqi Cao, Didong Li, Huafei Sun, Amir Assadi, Shiqiang Zhang. Efficient Weingarten Map and Curvature Estimation on Manifolds Mach Learn 110, 1319–1344 (2021). https://doi.org/10.1007/s10994-021-05953-4

Contact

Email: bityueqi@gmail.com