Novel View Synthesis (NVS) from unconstrained photo collections is challenging in computer graphics. Recently, 3D Gaussian Splatting (3DGS) has shown promise for photorealistic and real-time NVS of static scenes. Building on 3DGS, we propose an efficient point-based differentiable rendering framework for scene reconstruction from photo collections. Our key innovation is a residual-based spherical harmonic coefficients transfer module that adapts 3DGS to varying lighting conditions and photometric post-processing. This lightweight module can be pre-computed and ensures efficient gradient propagation from rendered images to 3D Gaussian attributes. Additionally, we observe that the appearance encoder and the transient mask predictor, the two most critical parts of NVS from unconstrained photo collections, can be mutually beneficial. We introduce a plug-and-play lightweight spatial attention module to simultaneously predict transient occluders and latent appearance representation for each image. After training and preprocessing, our method aligns with the standard 3DGS format and rendering pipeline, facilitating seamlessly integration into various 3DGS applications. Extensive experiments on diverse datasets show our approach outperforms existing approaches on the rendering quality of novel view and appearance synthesis with high converge and rendering speed.

翻译：从无约束照片集中进行新视角合成是计算机图形学中的一项挑战性任务。近年来，三维高斯溅射在静态场景的光照真实感与实时新视角合成方面展现出潜力。基于三维高斯溅射，我们提出了一种面向照片集场景重建的高效基于点的可微分渲染框架。我们的核心创新是一个基于残差的球谐系数迁移模块，该模块使三维高斯溅射能够适应变化的照明条件和光度后处理。这一轻量级模块可进行预计算，并确保从渲染图像到三维高斯属性的高效梯度传播。此外，我们观察到无约束照片集新视角合成中两个最关键的组成部分——外观编码器和瞬态掩码预测器——能够相互促进。我们引入了一个即插即用的轻量级空间注意力模块，以同时预测每张图像的瞬态遮挡物和潜在外观表示。经过训练与预处理，我们的方法符合标准三维高斯溅射格式与渲染流程，便于无缝集成到各类三维高斯溅射应用中。在多样化数据集上的大量实验表明，我们的方法在新视角渲染质量与外观合成方面优于现有方法，同时保持了高收敛速度与渲染效率。