摘要
近年来序列图像三维重建技术得到了重视,提出了很多算法。本文对Harris特征提取算法进行了改进,并在在序列图像中进行特征跟踪,在特征的稀疏重建和稠密重建后,使用估计样本一致性算法实现三维重建,提高了序列图像三维重建的精度。
In recent years,the sequence images 3D reconstruction technology has been an important issue.Many algorithms have been proposed.In this paper,the Harris feature extraction algorithm is improved.Then feature tracking algorithm is carried out.After sparse and dense reconstruction of features,M-Estimate Sample Consensus is used for three-dimensional reconstruction,which improves the accuracy of three-dimensional reconstruction of sequence images.
作者
陈瑞林
张晓燕
Chen Ruilin;Zhang Xiaoyan(School of Information Science and Technology,Tan Kah Kee College,Xiamen University,Zhangzhou Fujian,363105)
出处
《电子测试》
2022年第3期92-94,共3页
Electronic Test
基金
福建省自然科学基金(2020J01039)
漳州市科技计划项目(ZZ2020J04)
厦门大学嘉庚学院预研项目(YY2019L01)
厦门大学嘉庚学院大学生创新创业训练计划项目(202113469208)资助。
关键词
三维重建
特征点匹配
稀疏重建
稠密重建
3D reconstruction
Feature point matching
Sparse reconstruction
Dense reconstruction
