This paper presents a case study of reverse engineering closely-spaced free-form shapes. The raw point cloud data captured from a body scanner was processed to filter most noise and redundancy. They were used to gener...This paper presents a case study of reverse engineering closely-spaced free-form shapes. The raw point cloud data captured from a body scanner was processed to filter most noise and redundancy. They were used to generate meshes through triangulation of points. Upon removal of inconsistencies resulted from residual noise, the clean-up meshes were then used to reconstruct the free-form surfaces that represented a fabric layer and a human body surface. The solid produced between these two surfaces is the fabric-over-body model. It helped generate a FEA (finite-element analysis) mesh with quality checks, such as distortion and stretch, were performed for all the meshed tetrahedral elements. The purpose is to prepare a FEA-ready model for future CFD (computational fluid dynamics) analysis.展开更多
As point cloud of one whole vehicle body has the traits of large geometric dimension, huge data and rigorous reverse precision, one pretreatment algorithm on automobile body point cloud is put forward. The basic idea ...As point cloud of one whole vehicle body has the traits of large geometric dimension, huge data and rigorous reverse precision, one pretreatment algorithm on automobile body point cloud is put forward. The basic idea of the registration algorithm based on the skeleton points is to construct the skeleton points of the whole vehicle model and the mark points of the separate point cloud, to search the mapped relationship between skeleton points and mark points using congruence triangle method and to match the whole vehicle point cloud using the improved iterative closed point （ICP） algorithm. The data reduction algorithm, based on average square root of distance, condenses data by three steps, computing datasets＇ average square root of distance in sampling cube grid, sorting order according to the value computed from the first step, choosing sampling percentage. The accuracy of the two algorithms above is proved by a registration and reduction example of whole vehicle point cloud of a certain light truck.展开更多
文摘This paper presents a case study of reverse engineering closely-spaced free-form shapes. The raw point cloud data captured from a body scanner was processed to filter most noise and redundancy. They were used to generate meshes through triangulation of points. Upon removal of inconsistencies resulted from residual noise, the clean-up meshes were then used to reconstruct the free-form surfaces that represented a fabric layer and a human body surface. The solid produced between these two surfaces is the fabric-over-body model. It helped generate a FEA (finite-element analysis) mesh with quality checks, such as distortion and stretch, were performed for all the meshed tetrahedral elements. The purpose is to prepare a FEA-ready model for future CFD (computational fluid dynamics) analysis.
基金This project is supported by Provincial Technology Cooperation Program of Yunnan,China(No.2003EAAAA00D043).
文摘As point cloud of one whole vehicle body has the traits of large geometric dimension, huge data and rigorous reverse precision, one pretreatment algorithm on automobile body point cloud is put forward. The basic idea of the registration algorithm based on the skeleton points is to construct the skeleton points of the whole vehicle model and the mark points of the separate point cloud, to search the mapped relationship between skeleton points and mark points using congruence triangle method and to match the whole vehicle point cloud using the improved iterative closed point （ICP） algorithm. The data reduction algorithm, based on average square root of distance, condenses data by three steps, computing datasets＇ average square root of distance in sampling cube grid, sorting order according to the value computed from the first step, choosing sampling percentage. The accuracy of the two algorithms above is proved by a registration and reduction example of whole vehicle point cloud of a certain light truck.
