To address the challenges of scalability and rendering quality in large-scale scene reconstruction

a blockbased reconstruction framework based on three-dimensional Gaussian splatting(3DGS)is proposed.Methodologically

unmanned aerial vehicle(UAV)aerial photography sensors are used to capture scene image data.Scalability is improved through scene blocking

visibility camera selection

and progressive point cloud expansion.In terms of rendering

ray-Gaussian intersection and Gaussian density control are introduced to improve efficiency.A ConvKAN-based appearance decoupling module is used to alleviate uneven illumination.Training is optimized by combining color distortion and depth distortion with normal consistency loss.Results on datasets such as Mill-19

UrbanScene3D

and MatrixCity show that this method outperforms existing mainstream methods in both rendering quality and accuracy.Its strong generalization capabilities are also demonstrated using self-collected unmanned aerial vehicle videos.Conclusions demonstrate that this framework is capable of efficiently reconstructing large-scale scenes and achieving high-fidelity new view synthesis.