ScholarWorks@성균관대학교

초록

Generalized feed-forward Gaussian models have made significant strides in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets. However, these models often struggle to represent high-frequency details primarily due to the limited number of Gaussians. While the densification strategy from per-scene 3D Gaussian splatting can be adapted for feed-forward models, it typically requires tens of thousands of optimization steps to reconstruct fine details and can easily lead to overfitting in sparse-view scenarios. In this paper, we propose Generative Densification, an efficient and generalizable densification strategy specifically tailored for feedforward models. Instead of iteratively splitting and cloning raw Gaussian parameters, our method up-samples feature representations produced by feed-forward models and generates their corresponding fine Gaussians in a single forward pass, leveraging learned prior knowledge for enhanced generalization. Experimental results on both object-level and scene-level reconstruction tasks demonstrate that our method outperforms state-of-the-art approaches with comparable or smaller model sizes, achieving notable improvements in representing fine details.