With the rapid development of oblique photography (OP) in recent years, the accuracy of reality modeling has increased, which has led to a surge in computational complexity. To solve the problem, a lot of reality modeling software adopts the strategy of cluster parallel computing for modeling. In this paper, the regression analysis method is used to study the influence of the configuration of the compute nodes in the cluster, which aims at improving the computational efficiency of the cluster for the 3D reconstruction task. Furthermore, the M/M/S queuing model in queuing theory is used to model the multi-task assignment of the cluster, and the mathematical model between compute nodes and performance of the cluster is established, which achieves the effective quantitative evaluation of the cluster computing efficiency. Experiments show that the CPU performance of the compute nodes is the most critical hardware factor affecting the efficiency of the cluster.
The size of 3D model reconstructed based on oblique photography is always too large to load into Unity3D efficiently and robustly for roaming. To solve this problem, we propose a novel roaming method for oblique photography threedimensional models in Unity3D. The method can quickly load large-scale oblique photography model in Unity3D and realize fluency virtual roaming. Firstly, different level of detail models are generated by using LOD (level of detail) technology and divide the LOD models into blocks with same size. Secondly, we load the entire low LOD model as a panoramic view of the scene and load little high LOD model blocks around the location of viewpoint dynamically while roaming. A Nine-palace mode is adopted for high LOD model blocks selection strategy. Finally, a coroutines and asynchronous loading methods are used to further improve the roaming process. The experimental results show that our method is faster than Acute3D Viewer in the visualization of oblique photography model.