Efficient representation of 3D spatial data for defense-related applications
To address the challenge of balancing geometric accuracy and visual fidelity in large-scale 3D spatial data for defense applications, this paper proposes a hierarchical hybrid representation architecture integrating classical geometric modeling with neural rendering. The architecture employs triangle meshes and voxel grids to ensure foundational geometric fidelity, while leveraging 3D Gaussian splatting and Neural Radiance Fields (NeRF) for high-fidelity photorealistic rendering at the upper layer. A unified scene management framework enables multi-granularity co-optimization across representations. Compared to purely geometric or purely neural approaches, our method achieves significant improvements: 2.1× acceleration in computational efficiency and +3.7 dB PSNR gain in rendering quality—particularly beneficial for line-of-sight analysis, physics-based simulation, and real-time visualization. It supports scalable modeling and interactive rendering of scenes with up to hundreds of millions of polygons, establishing a new paradigm for military digital twins that simultaneously delivers geometric precision, computational efficiency, and visual realism.