特征点维度的静态场景三维重建运动目标剔除

基于语义分割的图像掩膜方法常用来解决静态场景三维重建任务中运动物体的干扰问题,然而利用掩膜成功剔除运动物体的同时会产生少量无效特征点.针对此问题,提出一种在特征点维度的运动目标剔除方法,利用卷积神经网络获取运动目标信息,并构建特征点过滤模块,使用运动目标信息过滤更新特征点列表,实现运动目标的完全剔除.通过采用地面图像和航拍图像两种数据集以及DeepLabV3、YOLOv4两种图像处理算法对所提方法进行验证,结果表明特征点维度的三维重建运动目标剔除方法可以完全剔除运动目标,不产生额外的无效特征点,且相较于图像掩膜方法平均缩短13.36%的点云生成时间,减小9.93%的重投影误差.

Moving Object Elimination for 3D Reconstruction of Static Scenes in Feature Point Dimension

The image masking method based on semantic segmentation is often used to solve the interference problem of moving objects in three-dimensional (3D) reconstruction tasks of static scenes. However, a small number of invalid feature points will be produced when the mask is used to eliminate moving objects. To solve this problem, a method for eliminating moving objects in the dimension of feature points is proposed. The convolutional neural network is used to obtain the moving target information, and the feature point filtering module is constructed. Then, the moving target information is used to filter and update the feature point list for the complete elimination of the moving target. The ground image dataset and aerial image dataset and the processing algorithms of DeepLabV3 and YOLOv4 are used to verify the proposed method. The results show that the moving object elimination method in 3D reconstruction in the feature point dimension can completely eliminate the moving object without generating additional invalid feature points. Compared with the image masking method, the proposed method shortens the point cloud generation time by 13.36% and reduces the reprojection error by 9.93% on average.