DarkGS: Learning Neural Illumination and 3D Gaussians Relighting for
  Robotic Exploration in the Dark
Tianyi Zhang Kaining Huang Weiming Zhi Matthew Johnson-Roberson
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.