RESUMO
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. The L1-median filter is robust to outliers and noise compared to the mean. The L1 norm is a way to measure the sparsity of a solution, and applying an L1 optimization to the point cloud can measure the sparsity of sharp features, producing clean point set surfaces with sharp features. We optimize the L1 minimization problem by using the proximal gradient descent algorithm. Experimental results show that our approach is comparable to the state-of-the-art methods, as it filters out 3D models with a high level of noise, but keeps their geometric features.
RESUMO
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. In this paper, we present a method for recognizing surface defects in 3D point clouds. Firstly, we propose a novel 3D local descriptor called the Model Point Feature Histogram (MPFH) for defect detection. Our descriptor is inspired from earlier descriptors such as the Point Feature Histogram (PFH). To construct the MPFH descriptor, the models that best fit the local surface and their normal vectors are estimated. For each surface model, its contribution weight to the formation of the surface region is calculated and from the relative difference between models of the same region a histogram is generated representing the underlying surface changes. Secondly, through a classification stage, the points on the surface are labeled according to five types of primitives and the defect is detected. Thirdly, the connected components of primitives are projected to a plane, forming a 2D image. Finally, 2D geometrical features are extracted and by a support vector machine, the defects are recognized. The database used is composed of 3D simulated surfaces and 3D reconstructions of defects in welding, artificial teeth, indentations in materials, ceramics and 3D models of defects. The quantitative and qualitative results showed that the proposed method of description is robust to noise and the scale factor, and it is sufficiently discriminative for detecting some surface defects. The performance evaluation of the proposed method was performed for a classification task of the 3D point cloud in primitives, reporting an accuracy of 95%, which is higher than for other state-of-art descriptors. The rate of recognition of defects was close to 94%.
RESUMO
This manuscript investigates the minimum perception thresholds for force and vibration stimuli in a simple movement pattern and using the same haptic device. The model was model derived from the well-known Up-Down Transformed Response Rule varying the force magnitude and the amplitude of vibration feedback. It was demonstrated that the vibration sensitivity was around fifteen times smaller than the force threshold. The results were compared with previous published studies for different tasks, experimental configurations and devices. We concluded that the type of task significantly affects human detection threshold for force and vibration feedback, and should be adapted for the design of a new haptic-based skill transfer system for minimally invasive surgery (MIS) using haptic guidance.
El presente artículo muestra los resultados de una investigación para determinar los umbrales de mínima percepción ante estímulos de fuerzas y vibraciones aplicados durante la realización de un movimiento simple y utilizando el mismo dispositivo háptico. El modelo utilizado se derivó del bien conocido método de Up-Down Transformed Response Rule donde la retroalimentación fue variada en términos de la magnitud de la fuerza y de la amplitud de la vibración. Se demostró que la percepción de vibración fue alrededor de quince veces más pequeña que el umbral de fuerzas y se compararon los resultados con trabajos previos para diferentes tareas, configuraciones experimentales y distintos dispositivos. Se concluyó que el tipo de tarea afecta significativamente el umbral de detección humano tanto para retroalimentaciones de fuerzas como de vibraciones. Además es fundamental considerar estos valores en el diseño de nuevos sistemas de guiado hápticos para el entrenamiento de habilidades requeridas en Cirugías Mínimamente Invasivas (MIS por sus siglas en Ingles).
RESUMO
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. (3) The bone and skin shape measurements are registered using titanium targets located on the head of the patient. (4) Using a manual segmentation technique the head and lower jaw geometry are extracted separately to deal with jaw motion at the different record visits. (5) Using natural features of the upper head the two datasets are then registered with each other and then compared to evaluate bone, teeth, and skin displacements before and after treatments. This procedure is now used at the University of Alberta orthodontic clinic.