Scene view synthesis, which generates novel views from limited perspectives, is increasingly vital for applications like virtual reality, augmented reality, and robotics. Unlike object-based tasks, such as generating 360° views of a car, scene view synthesis handles entire environments where non-uniform observations pose unique challenges for stable rendering quality. To address this issue, we propose a novel approach: renderability field-guided gaussian splatting (RF-GS). This method quantifies input inhomogeneity through a renderability field, guiding pseudo-view sampling to enhanced visual consistency. To ensure the quality of wide-baseline pseudo-views, we train an image restoration model to map point projections to visible-light styles. Additionally, our validated hybrid data optimization strategy effectively fuses information of pseudo-view angles and source view textures. Comparative experiments on simulated and real-world data show that our method outperforms existing approaches in rendering stability.
View on arXiv@article{jin2025_2504.19261,
title={ Rendering Anywhere You See: Renderability Field-guided Gaussian Splatting },
author={ Xiaofeng Jin and Yan Fang and Matteo Frosi and Jianfei Ge and Jiangjian Xiao and Matteo Matteucci },
journal={arXiv preprint arXiv:2504.19261},
year={ 2025 }
}