3D reconstruction for Digital Twins often relies on LiDAR-based methods, which provide accurate geometry but lack the semantics and textures naturally captured by cameras. Traditional LiDAR-camera fusion approaches require complex calibration and still struggle with certain materials like glass, which are visible in images but poorly represented in point clouds. We propose a camera-only pipeline that reconstructs scenes using 3D Gaussian Splatting from multi-view images, extracts semantic material masks via vision models, converts Gaussian representations to mesh surfaces with projected material labels, and assigns physics-based material properties for accurate sensor simulation in modern graphics engines and simulators. This approach combines photorealistic reconstruction with physics-based material assignment, providing sensor simulation fidelity comparable to LiDAR-camera fusion while eliminating hardware complexity and calibration requirements. We validate our camera-only method using an internal dataset from an instrumented test vehicle, leveraging LiDAR as ground truth for reflectivity validation alongside image similarity metrics.

翻译：数字孪生中的三维重建通常依赖于基于激光雷达的方法，这类方法能提供精确的几何结构，但缺乏相机自然捕捉的语义信息和纹理。传统的激光雷达-相机融合方法需要复杂的标定，并且对于某些材料（如玻璃）的处理仍然存在困难——这些材料在图像中可见，但在点云中表征不佳。我们提出了一种仅使用相机的流程：该方法利用多视角图像通过三维高斯溅射进行场景重建，通过视觉模型提取语义材料掩码，将高斯表示转换为带有投影材料标签的网格表面，并为现代图形引擎和模拟器中的精确传感器仿真分配基于物理的材料属性。该方法将逼真的重建与基于物理的材料分配相结合，提供了与激光雷达-相机融合相媲美的传感器仿真保真度，同时消除了硬件复杂性和标定要求。我们使用来自一辆装备仪器的测试车辆的内部数据集验证了我们的纯相机方法，在利用图像相似度指标的同时，将激光雷达数据作为反射率验证的基准真值。