用源图像提升高斯点渲染质量，实现细节与视角变化的精准还原。
IBGS: Image-Based Gaussian Splatting
- 基于源图像建模像素颜色残差，结合基础颜色与邻近图像信息。
- 在标准评测上显著超越现有方法，且不增加存储开销。
- 适合需要高保真渲染与视点依赖效果的场景重建任务。
3D高斯点绘图（3DGS）近期成为一种快速、高质量的新视角合成（NVS）方法。然而，其使用低阶球谐函数限制了对空间变化颜色及视点相关效应（如镜面高光）的捕捉能力。现有方法或采用全局纹理图（难以处理复杂场景），或为每个高斯点配置独立纹理图（带来高存储开销）。本文提出图像基高斯点绘图（IBGS），利用高分辨率源图像实现精细细节与视点特异性颜色建模。具体地，将每个像素颜色建模为标准3DGS渲染的基色与从邻近训练图像中学习到的残差之和。该设计促进表面精确定位，支持高频率细节与准确视点依赖效果的渲染。在标准NVS基准上的实验表明，本方法在渲染质量上显著优于先前高斯点绘图方法，且无需增加存储开销。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has recently emerged as a fast, high-quality method for novel view synthesis (NVS). However, its use of low-degree spherical harmonics limits its ability to capture spatially varying color and view-dependent effects such as specular highlights. Existing works augment Gaussians with either a global texture map, which struggles with complex scenes, or per-Gaussian texture maps, which introduces high storage overhead. We propose Image-Based Gaussian Splatting, an efficient alternative that leverages high-resolution source images for fine details and view-specific color modeling. Specifically, we model each pixel color as a combination of a base color from standard 3DGS rendering and a learned residual inferred from neighboring training images. This promotes accurate surface alignment and enables rendering images of high-frequency details and accurate view-dependent effects. Experiments on standard NVS benchmarks show that our method significantly outperforms prior Gaussian Splatting approaches in rendering quality, without increasing the storage footprint.
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