基于改进SIFT算法的三维重建技术研究  

Research on Three-dimensional Reconstruction Technology Based on Improved SIFT Algorithm

作　　者：李冉[1] 杨超宇[1] LI Ran;YANG Chaoyu(chool of Artificial Intelligence,Anhui University of Science and Technology,Anhui Huainan 232001,China)

机构地区：[1]安徽理工大学人工智能学院,安徽淮南232001

出　　处：《重庆工商大学学报(自然科学版)》2025年第1期105-111,共7页Journal of Chongqing Technology and Business University:Natural Science Edition

基　　金：国家自然科学基金项目资助(61873004).

摘　　要：目的针对三维重建过程中尺度不变特征转换(Scale Invariant Feature Transform,SIFT)算法对噪声敏感,导致特征点提取和匹配的错误和运行时间长等问题,提出一种改进的SIFT算法,旨在提高特征点提取的准确性和减少运行时间。方法改进的SIFT算法首先对图像的像素点进行遍历,对于每个目标像素点,将其与其8邻域内的像素点进行灰度值比较。如果相邻像素点的灰度值与目标像素点的灰度值之差小于设定的阈值,则将该相邻像素点标记为相似点;根据相似点的数量,确定目标像素点是否为兴趣点,如果相似点的数量满足特定条件,则将目标像素点判定为兴趣点,然后在以兴趣点为中心的区域内使用SIFT算法提取特征点。结果在不同的阈值设置和对不同尺寸图像进行对比实验中,结果显示改进的SIFT算法相较于传统的SIFT算法,在特征点提取正确率上有约10%左右的提升,运行时间节约25%左右。结论实验结果表明:本文提出的改进SIFT算法通过引入对噪声的抑制和对兴趣点的筛选,能够有效提升特征点的提取质量,以及特征点提取和匹配中的错误率,并且显著降低运行时间。Objective In response to the sensitivity of the scale invariant feature transform(SIFT)algorithm to noise during the three-dimensional reconstruction process,leading to errors in feature point extraction and matching as well as long runtime,an improved SIFT algorithm was proposed to enhance the accuracy of feature point extraction and reduce runtime.Methods The improved SIFT algorithm first traversed the pixels of the image.For each target pixel,it compared the grayscale values with those of its eight neighboring pixels.If the difference in grayscale values between adjacent pixels and the target pixel was less than a specified threshold,the adjacent pixel was marked as a similar point.Based on the number of similar points,whether the target pixel was an interest point was determined.If the number of similar points met specific conditions,the target pixel was determined as an interest point,and then the SIFT algorithm was used to extract feature points within the region centered on the interest point.Results In experiments comparing different threshold settings and images of different sizes,the results indicated that the improved SIFT algorithm achieved an approximate 10% increase in feature point extraction accuracy and saved around 25% of runtime compared with the traditional SIFT algorithm.Conclusion The experimental results demonstrate that the proposed improved SIFT algorithm effectively enhances the quality of feature point extraction by introducing noise suppression and interest point filtering,reducing the error rate in feature point extraction and matching,and significantly reducing runtime.

关 键 词：特征点提取 SIFT 三维重建 特征点匹配 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]