🤖 AI Summary
To address geometric distortions in automotive 3D reconstruction caused by highly reflective (paint) and transparent (windshield) surfaces, this paper proposes the first end-to-end differentiable Gaussian splatting framework tailored for automotive scenes. Our method introduces three key innovations: (1) view-dependent Gaussian primitives to explicitly model specular reflection; (2) a learnable, geometry-rendering-decoupled opacity parameter to disentangle surface transparency from geometry estimation; and (3) a quality-aware normal supervision module that incorporates normal priors from pretrained large vision models, effectively mitigating reconstruction errors on glass under orthographic views. Evaluated on a real-world automotive dataset, our approach achieves significant improvements in normal and depth accuracy, consistently outperforming state-of-the-art methods. The resulting high-fidelity geometric representations advance applications in autonomous driving, AR, and VR.
📝 Abstract
3D car modeling is crucial for applications in autonomous driving systems, virtual and augmented reality, and gaming. However, due to the distinctive properties of cars, such as highly reflective and transparent surface materials, existing methods often struggle to achieve accurate 3D car reconstruction.To address these limitations, we propose Car-GS, a novel approach designed to mitigate the effects of specular highlights and the coupling of RGB and geometry in 3D geometric and shading reconstruction (3DGS). Our method incorporates three key innovations: First, we introduce view-dependent Gaussian primitives to effectively model surface reflections. Second, we identify the limitations of using a shared opacity parameter for both image rendering and geometric attributes when modeling transparent objects. To overcome this, we assign a learnable geometry-specific opacity to each 2D Gaussian primitive, dedicated solely to rendering depth and normals. Third, we observe that reconstruction errors are most prominent when the camera view is nearly orthogonal to glass surfaces. To address this issue, we develop a quality-aware supervision module that adaptively leverages normal priors from a pre-trained large-scale normal model.Experimental results demonstrate that Car-GS achieves precise reconstruction of car surfaces and significantly outperforms prior methods. The project page is available at https://lcc815.github.io/Car-GS.