Sparse Regularization-Based Approach for Point Cloud Denoising and Sharp Features Enhancement.

Denoising the point cloud is fundamental for reconstructing high quality surfaces with details in order to eliminate noise and outliers in the 3D scanning process. The challenges for a denoising algorithm are noise reduction and sharp features preservation. In this paper, we present a new model to reconstruct and smooth point clouds that combine L1-median filtering with sparse L1 regularization for both denoising the normal vectors and updating the position of the points to preserve sharp features in the point cloud.

A Method for Automatic Surface Inspection Using a Model-Based 3D Descriptor.

Automatic visual inspection allows for the identification of surface defects in manufactured parts. Nevertheless, when defects are on a sub-millimeter scale, detection and recognition are a challenge. This is particularly true when the defect generates topological deformations that are not shown with strong contrast in the 2D image.

Long term three dimensional tracking of orthodontic patients using registered cone beam CT and photogrammetry.

The measurements from registered images obtained from Cone Beam Computed Tomography (CBCT) and a photogrammetric sensor are used to track three-dimensional shape variations of orthodontic patients before and after their treatments. The methodology consists of five main steps: (1) the patient's bone and skin shapes are measured in 3D using the fusion of images from a CBCT and a photogrammetric sensor. (2) The bone shape is extracted from the CBCT data using a standard marching cube algorithm.