一种基于场景图分割的混合式多视图三维重建方法

针对大范围三维重建, 重建效率较低和重建稳定性、精度差等问题, 提出了一种基于场景图分割的大范围混合式多视图三维重建方法.该方法首先使用多层次加权核K均值算法进行场景图分割; 然后，分别对每个子场景图进行混合式重建, 生成对应的子模型, 通过场景图分割、混合式重建和局部优化等方法提高重建效率、降低计算资源消耗, 并综合采用强化的最佳影像选择标准、稳健的三角测量方法和迭代优化等策略, 提高重建精度和稳健性; 最后, 对所有子模型进行合并, 完成大范围三维重建.分别使用互联网收集数据和无人机航拍数据进行了验证, 并与1DSFM、HSFM算法在计算精度和计算效率等方面进行了比较.实验结果表明, 本文算法大大提高了计算效率、计算精度, 能充分保证重建模型的完整性, 并具备单机大范围场景三维重建能力.

A Hybrid Multi-View 3D Reconstruction Method Based on Scene Graph Partition

To solve the problem of low computational efficiency and poor stability of large scale 3D reconstruction, a novel hybrid scheme of large scale reconstruction was proposed. Scene graph was partitioned by multi-level weighted kernel K-means algorithm at first; then sub-scenes were reconstructed by hybrid reconstruction producing sub-models, in which improved optimal image selection criteria, robust triangulation methods and iterative optimization strategies were adopted, and the computational efficiency was improved by using strategies of scene graph part, hybrid reconstruction and partial bundle adjustment (BA); Finally, All sub-models were merged into the final reconstruction result. Experiments were performed using images collected from the internet and UAV aerial images respectively, and comparison was made with 1DSFM and HSFM in terms of computation accuracy and computation efficiency. Experimental results demonstrate the proposed algorithm greatly improves computational efficiency and computational accuracy, fully ensures the integrity of the reconstructed scene and is able to reconstruct large scale scene in single computer.