Neural Radiance Fields for Unbounded Lunar Surface Scene

Accurate understanding of lunar surface topography is vital for effective decision-making and remote control of lunar rovers during exploration missions. Conventional sensing methods often struggle to capture the intricate details of the lunar landscape. In response, we propose an innovative approach that leverages NeRF to synthesize new viewpoints within the expansive lunar environment. By blending 3D hash grids and 2D plane grids representations, our approach provides a comprehensive scene representation. We employ the technique of spiral sampling and feature rendering to enhance rendering quality while simultaneously reducing training time. Additionally, we leverage sparse point cloud to aid the model in better learning the geometric structure of the lunar environment. Through experimentation, we have demonstrated that our method is capable of synthesizing realistic images of lunar environments.