جستجوی مقالات مرتبط با کلیدواژه "surface normal vector" در نشریات گروه "مکانیک"

Registration of point clouds is a key process in creating a digital model in reverse engineering. Registration is complex and ill-conditioned problem and these impede to achieve fully automatic comprehensive algorithm.in this study a new method to improve automation level is proposed. In this method, at first surface features are extracted from point clouds and then these data use for detecting correspondence points between point clouds. Registration process accuracy depends on carefully selected corresponding point between point clouds. In present research surface curvature and local shape are used for finding correct correspondence points. For feature extraction, surface curvature for each point of point clouds is calculated by using umbrella curvature and also a new method for determining local shape is presented. For each point of point cloud a shape number is determined. Determination of shape number is done by neighbors’ coordinates of point of concern. In this method, the corrected corresponding points are points that have almost equal umbrella curvature and shape number. Rigidity helps algorithm to find pairwise points. Analyzing results shows that the proposed algorithm performs well and has appropriate abilities on fully automatic registration of point clouds.

With the rapid development of 3D laser scanners, point-based discrete shape modeling is being widely used in many engineering applications, e.g. quality control, reverse engineering, computer graphics and machine vision. Point cloud discrete curvature estimation is considered basic operation in point cloud operations and is used in many applications related to cloud points. This paper presents a novel method for point clouds surface curvature estimation. One of the key components of point clouds surface curvature calculation is neighbor coordinates of query point. For selecting neighbors homogeneous neighborhood method is used. This method of choosing neighbors, in addition to the distance takes into consideration the directional balance by improving the k nearest neighbors. Surface normal vector is estimated by neighbors coordinates. In this paper surface curvature is calculated based on normal vector and homogeneous neighbors coordinates. Surface curvature calculated using the novel method is called umbrella curvature. To evaluate how this method performs, umbrella curvature values are calculated for a number of cloud points and the results are used in some different applications. The results show that the proposed method performs well in point clouds curvature estimation.

Point based 3D modeling has recently received greater attention، mainly due to its simplicity. One of the most fundamental operations for point set processing is to find the neighbors of each point in point clouds. This paper presents a new method called homogeneous neighborhood for determining neighbors in point clouds. This method of choosing neighbors، in addition to the distance takes into consideration the directional balance by improving the k nearest neighbors. The directional balance describes whether the neighbors are well spread around the point of concern. In this study effects of selecting neighbors on normal vector estimation are investigated. Normal vector is calculated using homogeneous neighborhood. For evaluation of the proposed method in determining neighbors، normal vector are calculated using the k nearest neighbors. The results show that the homogeneous neighborhood method is more accurate in normal vector estimation than the k nearest method. For evaluation of the homogeneous neighborhood method، it was employed in point cloud registration application. The results of registration by using the homogeneous neighborhood show that this method of neighbor selection yields reduced registration errors.