DualGS: Unified Geometry and Appearance Modeling with Dual Gaussian Fields

Recent advances in Gaussian Splatting have shown remarkable progress in neural scene representation and rendering. However, existing methods often face an inherent trade-off between geometric accuracy and photometric fidelity. While geometry-oriented approaches such as 2DGS focus on reconstructing thin and precise surfaces, they often suffer from limited rendering expressiveness. Conversely, appearance-oriented methods like 3DGS achieve photorealistic novel view synthesis but tend to produce blurred or inconsistent surfaces. In this work, we present DualGS, a unified framework that bridges this gap through a coarse-to-refine dual-branch design. Specifically, we introduce two specialized Gaussian fields, one dedicated to surface reconstruction and the other to appearance refinement, which are jointly optimized through edge-aware multi-view consistency constraints. The surface branch is guided by edge-aware photometric and feature consistency losses that leverage cross-view geometry cues, while the appearance branch enhances visual fidelity via rendering supervision. Furthermore, we introduce a cross-branch consistency mechanism, allowing the appearance field to provide auxiliary supervision for stabilizing surface optimization. Extensive experiments on standard benchmarks demonstrate that our method significantly improves reconstruction accuracy and rendering realism over state-of-the-art methods, achieving precise surfaces and high-fidelity appearance simultaneously. We will release our source code.