随见随渲染：可渲染性场引导的高斯泼溅
Rendering Anywhere You See: Renderability Field-guided Gaussian Splatting
AI总结：
针对场景视图合成中非均匀观测导致渲染质量不稳定的问题，提出可渲染性场引导的高斯泼溅（RF-GS）方法，通过量化输入不均匀性引导伪视图采样并融合图像恢复与混合数据优化策略，显著提升了渲染稳定性。
摘要：
场景视图合成从有限视角生成新视图，对虚拟现实、增强现实和机器人等应用日益重要。与生成汽车360°视图等基于物体的任务不同，场景视图合成处理的是整个环境，其中非均匀观测对稳定渲染质量构成了独特挑战。为解决此问题，我们提出了一种新方法：可渲染性场引导的高斯泼溅（RF-GS）。该方法通过可渲染性场量化输入不均匀性，引导伪视图采样以增强视觉一致性。为确保宽基线伪视图的质量，我们训练了一个图像恢复模型，将点投影映射为可见光风格。此外，我们验证的混合数据优化策略有效融合了伪视角与源视图纹理的信息。在模拟和真实数据上的对比实验表明，我们的方法在渲染稳定性上优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
