Enhancement of ridge-valley features in point cloud based on position and normal guidance.

• A simple and effective ridge valley point recognition method is present, which greatly reduces the number of potential feature points and improves the robustness of the recognition. • A freeform feature enhancement algorithm based on position and normal constraints is proposed. By different settings, several enhancement effects can be realized, such as preserving the original shape as much as possible, sharpen it or recovering a totally sharp edge. • A point cloud parameterization method based on feature line guidance is introduced to reduce the number of unknowns by 2/3 and eliminate the lateral sliding of points. [Display omitted] Ridge-valley features are important elements of a model. To recognize these features from point cloud, this paper introduces a new criterion named Extremal Point Distance (EPD) to greatly reduce the number of potential feature points and locate feature position more accurately. On this basis, a feature enhancement algorithm is proposed. The algorithm adjusts the coordinates of feature regions by minimizing a linear objective function consisting of expected position and normal, which can ensure the accurate sampling of feature position. We also present a parameterization method to eliminate the lateral sliding of feature points and reduce the number of unknowns in the objective function. Since the EPD criterion only depends on the changing trend, rather than the absolute value of the curvature, our algorithm can infer the expected position and normal with a large neighborhood radius, which makes it robust to noise. Experiments show that our algorithm can adjust the feature amplitude and sharpness freely, and achieve satisfactory results in feature recognition, feature enhancement and sharp feature restoration. [ABSTRACT FROM AUTHOR]