CoReSplat: Consensus-Relative Residual Compensation for Distractor-Free 3D Gaussian Splatting

Distractor-free 3D Gaussian Splatting (3DGS) aims to recover a static scene from real-world image collections containing dynamic distractors, which introduce inconsistent observations that can corrupt scene optimization and produce rendering artifacts. Existing methods mitigate distractors through reliability-aware supervision or explicit scene decomposition. However, photometric residuals induced by retained observations may still reflect both under-explained scene content and view-specific discrepancies, leaving it unclear how these residuals should guide shared-scene updates. To address this supervision-to-update ambiguity, we propose CoReSplat, a consensus-relative residual compensation framework that leverages cross-view consensus over residual histories to identify disagreement for training-time compensation. Specifically, CoReSplat maintains a per-view residual history to accumulate cross-iteration evidence of residual tendencies. It then aggregates geometrically corresponding residual histories into a cross-view consensus reference that captures their agreement across views. The departure of each view’s residual history from this reference defines its consensus-relative disagreement, which is applied as a stop-gradient additive correction to the current prediction before backpropagation. In this way, CoReSplat dynamically reshapes the effective photometric residual relative to the cross-view consensus, while reducing the immediate influence of view-specific discrepancies on scene updates. Experiments on two real-world benchmarks demonstrate improved novel-view synthesis and consistent performance gains when CoReSplat is integrated into representative distractor-free 3DGS baselines.