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.

翻译：人类即使在光照有限或变化的条件下也能构建出一致的环境心理模型。我们希望赋予机器人同样的能力。本文针对光照条件差且存在移动光源的场景下构建逼真场景表征的挑战展开研究。我们将光照建模视为一个学习问题，并利用所开发的照明模型辅助场景重建。我们提出了一种创新框架，该方法采用数据驱动的神经光仿真器对相机-照明系统进行建模与标定。进一步地，我们提出了DarkGS方法，该方法应用神经光仿真器构建可重光照的三维高斯场景模型，能够从新视角实现实时逼真渲染。我们通过多种真实环境实验验证了所提仿真器与系统的适用性和鲁棒性。