单目视频图像序列三维重建方法  

Three-Dimensional Reconstruction Method of Monocular Video Image Sequences

作　　者：沙晓鹏 曹加奇 李文静 秦晔 SHA Xiao-peng;CAO Jia-qi;LI Wen-jing;QIN Ye(School of Control Engineering,Northeastern University at Qinhuangdao,Qinhuangdao 066004,China)

机构地区：[1]东北大学秦皇岛分校控制工程学院,河北秦皇岛066004

出　　处：《东北大学学报(自然科学版)》2024年第12期1680-1687,共8页Journal of Northeastern University(Natural Science)

基　　金：河北省自然科学基金资助项目(F2021501021)。

摘　　要：针对单目场景下的三维重建,由于运动模糊和小基线图像的存在导致图像模糊、特征点匹配低、重建精度差等问题,提出一种单目视觉系统的三维重建方法 .首先,根据图像的边缘信息利用小波变换检测原始数据中的模糊图像并剔除,通过几何鲁棒性准则筛选出宽基线图像,得到用于三维重建的清晰图像;其次,提出了基于区域划分的错误匹配特征点剔除算法,剔除重复匹配和错误匹配特征点;最后,提出3种不同区域增长方式获取更多特征点进行三维点计算.结果表明,提出的方法有效地去除了错误匹配特征点,提高了特征点匹配的准确率,获得了更多的点云数量,提高了重建模型的精度和完整度.For 3D reconstruction in uncalibrated monocular scene,due to the existence of motion blur and small baseline image,there are still problems such as blurred image,low feature matching,and reconstruction accuracy.A 3D reconstruction method of monocular vision system is proposed.Firstly,the fuzzy image in the original data is detected and removed by wavelet transform algorithm according to the edge information of the image,and the wide baseline image is screened by geometric robustness criterion to obtain clear images for 3D reconstruction.Then,an algorithm for eliminating mismatched feature points based on region division is proposed to eliminate repeated matching and mismatched feature points.Finally,three different regional growth methods are proposed to obtain more feature points for three-dimensional point calculation.The results show that the proposed method can effectively remove mismatched feature points,improve the accuracy of feature point matching,obtain more point clouds,and improve the integrity and accuracy of the reconstructed model.

关 键 词：三维重建 关键帧筛选 特征提取 稀疏稠密化 区域增长 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]