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.

本文提出了一种名为高斯时间机器（Gaussian Time Machine，GTM）的方法，通过使用离散化的时间嵌入向量由轻量级的多层感知机（Multi-Layer-Perceptron，MLP）解码来建模高斯图元的时间依赖属性。通过调整高斯图元的不透明度，我们可以重建物体的可见性变化，并进一步提出了一种分解颜色模型以改善几何一致性。GTM 在三个数据集上实现了最先进的渲染保真度，并且在渲染方面比基于 NeRF 的对应方法快 100 倍。此外，GTM 成功地解开外观变化并呈现平滑的外观插值。