Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D Car Reconstruction
Journal:
arXiv
Published Date:
Jan 19, 2025
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.
