For reverse engineering a CAD model, it is necessary to integrate measured points from several views of an object into a common reference frame. Given a rough initial alignment of point cloud in different views with p...For reverse engineering a CAD model, it is necessary to integrate measured points from several views of an object into a common reference frame. Given a rough initial alignment of point cloud in different views with point-normal method, further refinement is achieved by using an improved iterative closest point (ICP) algorithm. Compared with other methods used for mult-view registration, this approach is automatic because no geometric feature, such as line, plane or sphere needs to be extracted from the original point cloud manually. A good initial alignment can be acquired automatically and the registration accuracy and efficiency is proven better than the normal point-point ICP algorithm both experimentally and theoretically.展开更多
针对时间反演多址系统中信道的相关性会导致多用户干扰的问题,以降低用户间干扰和算法复杂度为目标,提出基于Barzilai-Borwein的共轭梯度迭代检测算法。首先通过共轭梯度迭代两次找到最速下降方向,然后通过Barzilai-Borwein沿着共轭梯...针对时间反演多址系统中信道的相关性会导致多用户干扰的问题,以降低用户间干扰和算法复杂度为目标,提出基于Barzilai-Borwein的共轭梯度迭代检测算法。首先通过共轭梯度迭代两次找到最速下降方向,然后通过Barzilai-Borwein沿着共轭梯度搜索的方向继续迭代。仿真表明,所提算法收敛速度快于Barzilai-Borwein和共轭梯度算法,且复杂度低于共轭梯度算法和最小均方误差(minimum mean square error,MMSE)算法,保持在O(N2)。展开更多
基金the National Natural Science Foundation of China (59990470) and the NationalOutstanding Young Scientist Foundation of China (
文摘For reverse engineering a CAD model, it is necessary to integrate measured points from several views of an object into a common reference frame. Given a rough initial alignment of point cloud in different views with point-normal method, further refinement is achieved by using an improved iterative closest point (ICP) algorithm. Compared with other methods used for mult-view registration, this approach is automatic because no geometric feature, such as line, plane or sphere needs to be extracted from the original point cloud manually. A good initial alignment can be acquired automatically and the registration accuracy and efficiency is proven better than the normal point-point ICP algorithm both experimentally and theoretically.
文摘针对时间反演多址系统中信道的相关性会导致多用户干扰的问题,以降低用户间干扰和算法复杂度为目标,提出基于Barzilai-Borwein的共轭梯度迭代检测算法。首先通过共轭梯度迭代两次找到最速下降方向,然后通过Barzilai-Borwein沿着共轭梯度搜索的方向继续迭代。仿真表明,所提算法收敛速度快于Barzilai-Borwein和共轭梯度算法,且复杂度低于共轭梯度算法和最小均方误差(minimum mean square error,MMSE)算法,保持在O(N2)。
