  Ridge-valley features are important elements of point clouds, as they contain
rich surface information. To recognize these features from point clouds, this
paper introduces an extreme point distance (EPD) criterion with scale
independence. Compared with traditional methods, the EPD greatly reduces the
number of potential feature points and improves the robustness of multiscale
feature point recognition. On this basis, a feature enhancement algorithm based
on user priori guidance is proposed that adjusts the coordinates of the feature
area by solving an objective equation containing the expected position and
normal constraints. Since the expected normal can be expressed as a function of
neighborhood point coordinates, the above objective equation can be converted
into linear sparse equations with enhanced feature positions as variables, and
thus, the closed solution can be obtained. In addition, a parameterization
method for scattered point clouds based on feature line guidance is proposed,
which reduces the number of unknowns by 2/3 and eliminates lateral sliding in
the direction perpendicular to feature lines. Finally, the application of the
algorithm in multiscale ridge-valley feature recognition, freeform surface
feature enhancement and computer-aided design (CAD) workpiece sharp feature
restoration verifies its effectiveness.
