🤖 AI Summary
This work addresses the geometric and photometric degradation in 3D Gaussian Splatting (3DGS) under sparse-view settings by proposing a unified inpainting framework leveraging video diffusion models. The authors construct a large-scale video dataset comprising 107.5K paired samples and introduce an isomorphic dual-model architecture featuring fine-grained 3DGS artifact classification and an Artifact-Aware Triplet Fusion mechanism guided by artifact heatmaps. For the first time, intensity-aware restoration is integrated into the self-attention structure, enabling precise spatiotemporal consistent inpainting. The proposed method significantly outperforms existing approaches in sparse novel-view synthesis and robust 3D reconstruction, effectively enhancing multi-view consistency and generalization capability.
📝 Abstract
3D Gaussian Splatting (3DGS) delivers high-fidelity real-time rendering but suffers from geometric and photometric degradations under sparse-view constraints. Current generative restoration approaches are often limited by insufficient temporal coherence, a lack of explicit spatial constraints, and a lack of large-scale training data, resulting in multi-view inconsistencies, erroneous geometric hallucinations, and limited generalization to diverse real-world artifact distributions. In this paper, we present ArtifactWorld, a framework that resolves 3DGS artifact repair through systematic data expansion and a homogeneous dual-model paradigm. To address the data bottleneck, we establish a fine-grained phenomenological taxonomy of 3DGS artifacts and construct a comprehensive training set of 107.5K diverse paired video clips to enhance model robustness. Architecturally, we unify the restoration process within a video diffusion backbone, utilizing an isomorphic predictor to localize structural defects via an artifact heatmap. This heatmap then guides the restoration through an Artifact-Aware Triplet Fusion mechanism, enabling precise, intensity-guided spatio-temporal repair within native self-attention. Extensive experiments demonstrate that ArtifactWorld achieves state-of-the-art performance in sparse novel view synthesis and robust 3D reconstruction. Code and dataset will be made public.