基于可见内容引导的反射高斯溅射
Gaussian Splatting with Reflections Guided by What is Seen
AI总结：
本文提出基于2D高斯溅射的反射建模框架，利用输入图像直接观测信息，结合混合反射外观表示与视图相关镜面建模，在近场反射场景的新视图合成上实现了更优的渲染与几何精度。
摘要：
尽管神经辐射场（NeRF）和3D高斯溅射（3DGS）等新视图合成方法在普通场景上已展现出出色性能，但它们往往难以捕捉视图相关的镜面反射效果，尤其是近场反射。现有方法要么使用仅表示无穷远光线的环境贴图，要么使用仅依赖反射间接监督的环境高斯，均不足以准确建模此类效果。本文提出一种基于2D高斯溅射（2DGS）的框架，利用输入图像中直接可观测的环境信息，还整合了混合反射外观表示与视图相关镜面建模。在公共基准及新整理的近场反射数据集上的实验表明，该方法实现了更优的渲染质量与几何精度。
英文摘要：
While novel view synthesis methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have shown impressive performance on common scenes, they often struggle with capturing view-dependent specular effects, particularly near-field reflections. Existing methods utilize environment maps, which only represent lights from infinity, or environment Gaussians, which rely solely on indirect supervision from reflections, are insufficient for accurately modeling such effects. Here, we propose a framework based on 2D Gaussian Splatting (2DGS) that leverages directly observable environment information in input images. Our approach also integrates a hybrid reflection appearance representation and view-dependent specular modeling. Experiments on public benchmarks and a newly curated near-field reflection dataset demonstrate that our method achieves superior rendering quality and geometric accuracy.
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。
