  Recent advancements in neural rendering techniques have significantly
enhanced the fidelity of 3D reconstruction. Notably, the emergence of 3D
Gaussian Splatting (3DGS) has marked a significant milestone by adopting a
discrete scene representation, facilitating efficient training and real-time
rendering. Several studies have successfully extended the real-time rendering
capability of 3DGS to dynamic scenes. However, a challenge arises when training
images are captured under vastly differing weather and lighting conditions.
This scenario poses a challenge for 3DGS and its variants in achieving accurate
reconstructions. Although NeRF-based methods (NeRF-W, CLNeRF) have shown
promise in handling such challenging conditions, their computational demands
hinder real-time rendering capabilities. In this paper, we present Gaussian
Time Machine (GTM) which models the time-dependent attributes of Gaussian
primitives with discrete time embedding vectors decoded by a lightweight
Multi-Layer-Perceptron(MLP). By adjusting the opacity of Gaussian primitives,
we can reconstruct visibility changes of objects. We further propose a
decomposed color model for improved geometric consistency. GTM achieved
state-of-the-art rendering fidelity on 3 datasets and is 100 times faster than
NeRF-based counterparts in rendering. Moreover, GTM successfully disentangles
the appearance changes and renders smooth appearance interpolation.
