Humans have the remarkable ability to construct consistent mental models of
an environment, even under limited or varying levels of illumination. We wish
to endow robots with this same capability. In this paper, we tackle the
challenge of constructing a photorealistic scene representation under poorly
illuminated conditions and with a moving light source. We approach the task of
modeling illumination as a learning problem, and utilize the developed
illumination model to aid in scene reconstruction. We introduce an innovative
framework that uses a data-driven approach, Neural Light Simulators (NeLiS), to
model and calibrate the camera-light system. Furthermore, we present DarkGS, a
method that applies NeLiS to create a relightable 3D Gaussian scene model
capable of real-time, photorealistic rendering from novel viewpoints. We show
the applicability and robustness of our proposed simulator and system in a
variety of real-world environments.

我们介绍了一种新的框架，使用数据驱动方法来模拟和校准摄像机 - 光源系统，并应用于创建一种可重新照明的 3D 高斯场景模型，从而实现了从新的视点进行实时、逼真渲染的能力，并展示了我们所提出的模拟器和系统在各种实际环境中的适用性和强健性。