  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.
