GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction
Mulin Yu Tao Lu Linning Xu Lihan Jiang Yuanbo Xiangli Bo Dai
Presenting a 3D scene from multiview images remains a core and long-standing
challenge in computer vision and computer graphics. Two main requirements lie
in rendering and reconstruction. Notably, SOTA rendering quality is usually
achieved with neural volumetric rendering techniques, which rely on aggregated
point/primitive-wise color and neglect the underlying scene geometry. Learning
of neural implicit surfaces is sparked from the success of neural rendering.
Current works either constrain the distribution of density fields or the shape
of primitives, resulting in degraded rendering quality and flaws on the learned
scene surfaces. The efficacy of such methods is limited by the inherent
constraints of the chosen neural representation, which struggles to capture
fine surface details, especially for larger, more intricate scenes. To address
these issues, we introduce GSDF, a novel dual-branch architecture that combines
the benefits of a flexible and efficient 3D Gaussian Splatting (3DGS)
representation with neural Signed Distance Fields (SDF). The core idea is to
leverage and enhance the strengths of each branch while alleviating their
limitation through mutual guidance and joint supervision. We show on diverse
scenes that our design unlocks the potential for more accurate and detailed
surface reconstructions, and at the meantime benefits 3DGS rendering with
structures that are more aligned with the underlying geometry.