  Novel view synthesis from unconstrained in-the-wild images remains a
meaningful but challenging task. The photometric variation and transient
occluders in those unconstrained images make it difficult to reconstruct the
original scene accurately. Previous approaches tackle the problem by
introducing a global appearance feature in Neural Radiance Fields (NeRF).
However, in the real world, the unique appearance of each tiny point in a scene
is determined by its independent intrinsic material attributes and the varying
environmental impacts it receives. Inspired by this fact, we propose Gaussian
in the wild (GS-W), a method that uses 3D Gaussian points to reconstruct the
scene and introduces separated intrinsic and dynamic appearance feature for
each point, capturing the unchanged scene appearance along with dynamic
variation like illumination and weather. Additionally, an adaptive sampling
strategy is presented to allow each Gaussian point to focus on the local and
detailed information more effectively. We also reduce the impact of transient
occluders using a 2D visibility map. More experiments have demonstrated better
reconstruction quality and details of GS-W compared to NeRF-based methods, with
a faster rendering speed. Video results and code are available at
https://eastbeanzhang.github.io/GS-W/.
