GAD-Splat: Geometry-Appearance Decoupled Reconstruction for Distractor-Free 3D Gaussian Splatting

Transient objects disrupt multi-view consistency and limit observations of static scenes, leading to ghosting and blurring artifacts in 3D Gaussian Splatting (3DGS) reconstructions. Existing approaches primarily suppress unreliable supervision or separate static and transient scene components. However, minimizing photometric residuals can still favor incorrect Gaussian configurations that fit observed colors but misrepresent static surfaces, particularly where unmasked observations are limited. We introduce GAD-Splat, which estimates masking probabilities from motion cues and rendering residuals, then selects candidate masks from a segmentation model according to the likelihood of their union. Cross-view Depth Regularization (CDR) fuses filtered depth predictions into a shared truncated signed distance field (TSDF). Reference depths from this volume constrain rendered expected depth through surface and free-space losses. With geometry fixed, Cross-view Appearance Transfer (CAT) transfers color residuals computed from unmasked source observations and running averages of their renderings. These residuals complement color estimates for the target viewing direction to provide auxiliary supervision for masked pixels. This staged optimization aims to remove transient artifacts while preserving static scene content. Experiments on public benchmarks and self-captured datasets show effective transient-object removal and improved rendered image quality.