# High performance Lunar landing simulations

arXiv: https://arxiv.org/abs/2409.11450 · HTML: https://mlsanity.com/paper/2409.11450/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2024.

## Built on by (most cited first, 2 of 2)

- [Adapting Stereo Vision From Objects To 3D Lunar Surface Reconstruction with the StereoLunar Dataset](https://mlsanity.com/paper/2510.18172/) (2025)
- [Lunar-G2R: Geometry-to-Reflectance Learning for High-Fidelity Lunar BRDF Estimation](https://mlsanity.com/paper/2601.10449/) (2026)

## Abstract (arXiv)

Autonomous precision navigation to land onto the Moon relies on vision sensors. Computer vision algorithms are designed, trained and tested using synthetic simulations. High quality terrain models have been produced by Moon orbiters developed by several nations, with resolutions ranging from tens or hundreds of meters globally down to few meters locally. The SurRender software is a powerful simulator able to exploit the full potential of these datasets in raytracing. New interfaces include tools to fuse multi-resolution DEMs and procedural texture generation. A global model of the Moon at 20m resolution was integrated representing several terabytes of data which SurRender can render continuously and in real-time. This simulator will be a precious asset for the development of future missions.