稀疏点云配准测试数据

给出一种面向胫骨配准的快速轴向扰动寻优算法，该算法基于ICP算法融合体表约束点，施行最速下降区域优化搜索策略，可在小创伤切口手术中获取鲁棒性的优化配准结果。本数据集在NDI双目视觉下对胫骨模型开展10次点云数据采集，并存储为txt文本数据，其中每行数据代表点的三维空间位置。We propose a fast axial perturbation optimization algorithm for tibial registration. This algorithm integrates surface constraint points based on the Iterative Closest Point (ICP) algorithm and implements the steepest descent regional optimization search strategy, enabling it to obtain robust optimal registration results during minimally invasive incision surgery. This dataset collected 10 sets of point cloud data from tibial models via NDI binocular vision, and stored all data as TXT text files, where each line represents the three-dimensional spatial coordinates of a point.

搜集汇总

背景与挑战

背景概述

该数据集提供了稀疏点云配准测试数据，主要用于胫骨配准的快速轴向扰动寻优算法研究，该算法基于ICP算法融合体表约束点，适用于小创伤切口手术中的鲁棒优化。数据在NDI双目视觉下采集自胫骨模型，包含10次点云采集结果，以txt格式存储三维空间位置点。