  Photographs captured in unstructured tourist environments frequently exhibit
variable appearances and transient occlusions, challenging accurate scene
reconstruction and inducing artifacts in novel view synthesis. Although prior
approaches have integrated the Neural Radiance Field (NeRF) with additional
learnable modules to handle the dynamic appearances and eliminate transient
objects, their extensive training demands and slow rendering speeds limit
practical deployments. Recently, 3D Gaussian Splatting (3DGS) has emerged as a
promising alternative to NeRF, offering superior training and inference
efficiency along with better rendering quality. This paper presents Wild-GS, an
innovative adaptation of 3DGS optimized for unconstrained photo collections
while preserving its efficiency benefits. Wild-GS determines the appearance of
each 3D Gaussian by their inherent material attributes, global illumination and
camera properties per image, and point-level local variance of reflectance.
Unlike previous methods that model reference features in image space, Wild-GS
explicitly aligns the pixel appearance features to the corresponding local
Gaussians by sampling the triplane extracted from the reference image. This
novel design effectively transfers the high-frequency detailed appearance of
the reference view to 3D space and significantly expedites the training
process. Furthermore, 2D visibility maps and depth regularization are leveraged
to mitigate the transient effects and constrain the geometry, respectively.
Extensive experiments demonstrate that Wild-GS achieves state-of-the-art
rendering performance and the highest efficiency in both training and inference
among all the existing techniques.
