用神经辐射场生成真实多样的测试图像，提升自动驾驶系统视觉模块的鲁棒性。
NeRF-To-Real Tester: Neural Radiance Fields as Test Image Generators for Vision of Autonomous Systems
- 利用神经辐射场生成逼真且多样的测试图像。
- 在8个AUV/UAV视觉组件上验证，显著提升测试覆盖度。
- 适合自动驾驶系统感知模块的可靠性测试与验证。
陆地与水下基础设施自主检测市场迅速增长，应用于建筑勘测、工厂监控及海上风电场环境变化追踪。然而，自主水下航行器（AUV）和无人机（UAV）在仿真中过拟合控制器，导致实际运行性能不佳。亟需更丰富、更真实的测试数据来模拟真实挑战。本文提出N2R-Tester，利用神经辐射场（Neural Radiance Fields）生成逼真的测试图像，并集成至元测试框架中，用于视觉组件如vSLAM和目标检测的测试。该工具支持自定义场景训练，并可从扰动位置渲染测试图像。在8种不同视觉组件上的实验评估表明，该方法有效且具备广泛适用性。
原文摘要 · Abstract (English)
Autonomous inspection of infrastructure on land and in water is a quickly growing market, with applications including surveying constructions, monitoring plants, and tracking environmental changes in on- and off-shore wind energy farms. For Autonomous Underwater Vehicles and Unmanned Aerial Vehicles overfitting of controllers to simulation conditions fundamentally leads to poor performance in the operation environment. There is a pressing need for more diverse and realistic test data that accurately represents the challenges faced by these systems. We address the challenge of generating perception test data for autonomous systems by leveraging Neural Radiance Fields to generate realistic and diverse test images, and integrating them into a metamorphic testing framework for vision components such as vSLAM and object detection. Our tool, N2R-Tester, allows training models of custom scenes and rendering test images from perturbed positions. An experimental evaluation of N2R-Tester on eight different vision components in AUVs and UAVs demonstrates the efficacy and versatility of the approach.
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