Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D Car Reconstruction

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 face challenges in accurately reconstructing 3D models. 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.

We initialize View-Dependent Gaussians (VDG) and View-Shared Gaussians (VSG) using monocular depth estimates aligned with Structure-from-Motion (SFM). VDG models view-specific attributes, while VSG captures shared information. During rendering, a learnable hybrid opacity is applied to RGB images and depth/normal maps. Additionally, a quality-aware supervision module leverages pretrained normal priors to regulate rendered normals, especially in reflective regions.