Efficient representation of 3D spatial data for defense-related applications

2025-10-27

Artificial Intelligence for Security and Defence Applications III

AI Summary

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.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Geospatial sensor data is essential for modern defense and security, offering indispensable 3D information for situational awareness. This data, gathered from sources like lidar sensors and optical cameras, allows for the creation of detailed models of operational environments. In this paper, we provide a comparative analysis of traditional representation methods, such as point clouds, voxel grids, and triangle meshes, alongside modern neural and implicit techniques like Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS). Our evaluation reveals a fundamental trade-off: traditional models offer robust geometric accuracy ideal for functional tasks like line-of-sight analysis and physics simulations, while modern methods excel at producing high-fidelity, photorealistic visuals but often lack geometric reliability. Based on these findings, we conclude that a hybrid approach is the most promising path forward. We propose a system architecture that combines a traditional mesh scaffold for geometric integrity with a neural representation like 3DGS for visual detail, managed within a hierarchical scene structure to ensure scalability and performance.

Problem

• Comparing traditional and modern 3D representation methods for defense applications
• Analyzing trade-offs between geometric accuracy and photorealistic visual fidelity
• Proposing hybrid architecture combining mesh scaffolds with neural representations

Innovation

• Hybrid system combining mesh scaffold with neural representation
• Hierarchical scene structure ensuring scalability and performance
• Comparative analysis of traditional and modern 3D representation techniques