影像三维重建的网格自适应快速优化

Adaptive Fast Mesh Refinement of 3D Reconstruction Based on Image Information

针对倾斜影像大场景三维重建过程中数据量大导致的三角网格优化效率低的问题,提出了一种平衡网格优化精度与优化效率的网格自适应快速优化方法。根据影像灰度信息计算沿着三角面法向量方向顶点梯度的初始值,根据每一个三角形的不同计算结果将三角形标记为活跃三角形与怠惰三角形,通过对网格中活跃三角形优化及放弃怠惰三角形优化的策略,在获得网格顶点最佳位置的同时自适应地实现优化效率的快速提升。标准三维重建影像数据集及真实无人机倾斜影像三维重建网格优化的实验结果表明了此方法的有效性。网格模型优化时间及精度对比的结果表明,该方法在优化三角网格的同时,较大幅度地提高了网格优化的计算效率,从而快速获得三维重建模型。

A method of balancing the optimize accuracy and efficiency of the mesh to achieve mesh adaptive fast optimization is proposed to solve the problem of low efficiency of mesh optimization in the existing 3 D reconstruction based on images.After calculating the initial value of the vertex gradient along the triangular normal vector based on the image gray information,the mesh of each triangle is marked differently according to the different results:The active triangles and the lazy triangles,through the active in the mesh is refined to abandon the optimization of the lazy triangles in exchanging for improvement of efficiency.The optimization of the efficiency is fast improving while obtaining better mesh vertices.We choose classical images church and fountain data in the 3 D reconstruction experiment of computer vision and the widely used of unmanned aerial vehicle(UAV)images in photogrammetry to reconstruct mesh to refine.The mesh model optimization time and precision comparison results show that this algorithm can greatly improve the mesh optimize efficiency to obtain the 3 D model faster.