Research on the Technical Path to Optimize the Reconstruction of Large-Scale Historical Scenes in Historical Documentaries Using Parallel Distributed Computing Techniques

Abstract

The continuous development of distributed computing technology provides a new path for high-precision reconstruction of historical scenes. In this paper, we propose a technical optimization framework based on parallel distributed computing for the problems of low efficiency and lack of clarity in the reconstruction of large-scale historical scenes in historical documentaries. By integrating digital elevation model (DEM) and 3D Gaussian sputtering (3DGS) methods, combined with the DAG scheduling mechanism of Apache Spark framework and multi-factor weighted resilient distributed dataset (RDD) caching strategy, the reconstruction speed and rendering quality of the scene are significantly improved. The experiments show that the parallel computing nodes are set to 15, the nodes adopt an interval of 1500m, and the number of single file computations is 11 times to obtain higher modeling efficiency. The ambiguity of scene reconstruction with the introduction of parallel distributed computing is reduced to less than 17%, and the average ambiguity is lower than 16%. The values of three evaluation indexes are better than those of the comparison algorithms.