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.

基于 3D 高斯扩散的新视角合成方法在各种场景下表现出了良好的性能，本文提出了一种高效的基于点的可微渲染框架，用于从照片集合中重建场景，通过引入基于残差的球谐系数传输模块，将 3D 高斯扩散方法适应于不同的光照条件和光度后处理，同时引入了一个轻量级的空间注意力模块，可同时预测暂时遮挡物和隐含外观表示，实验结果表明，该方法在新视角和外观合成的渲染质量上优于现有方法，并拥有较高的收敛速度和渲染速度。