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arXiv · 2511.20348

Material-informed Gaussian Splatting for 3D World Reconstruction in a Digital Twin

Andy Huynh·João Malheiro Silva·Holger Caesar·Tong Duy Son
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Abstract

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.

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Andy Huynh, João Malheiro Silva, Holger Caesar, Tong Duy Son. 2025-11-25. Material-informed Gaussian Splatting for 3D World Reconstruction in a Digital Twin. https://arxiv.org/abs/2511.20348

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