# Scientific image rendering for space scenes with the SurRender software

arXiv: https://arxiv.org/abs/1810.01423 · HTML: https://mlsanity.com/paper/1810.01423/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2018.

## Built on by (most cited first, 8 of 8)

- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [Image simulation for space applications with the SurRender software](https://mlsanity.com/paper/2106.11322/) (2021)
- [Training Datasets Generation for Machine Learning: Application to Vision Based Navigation](https://mlsanity.com/paper/2409.11383/) (2024)
- [NaRPA: Navigation and Rendering Pipeline for Astronautics](https://mlsanity.com/paper/2211.01566/) (2022)
- [Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling Autonomy](https://mlsanity.com/paper/2401.12414/) (2024)
- [Addressing Camera Sensors Faults in Vision-Based Navigation: Simulation and Dataset Development](https://mlsanity.com/paper/2507.02602/) (2025)
- [Lunar-G2R: Geometry-to-Reflectance Learning for High-Fidelity Lunar BRDF Estimation](https://mlsanity.com/paper/2601.10449/) (2026)
- [POLAR-Sim: Augmenting NASA's POLAR Dataset for Data-Driven Lunar Perception and Rover Simulation](https://mlsanity.com/paper/2309.12397/) (2023)

## Abstract (arXiv)

Spacecraft autonomy can be enhanced by vision-based navigation (VBN) techniques. Applications range from manoeuvers around Solar System objects and landing on planetary surfaces, to in-orbit servicing or space debris removal. The development and validation of VBN algorithms relies on the availability of physically accurate relevant images. Yet archival data from past missions can rarely serve this purpose and acquiring new data is often costly. The SurRender software is an image simulator that addresses the challenges of realistic image rendering, with high representativeness for space scenes. Images are rendered by raytracing, which implements the physical principles of geometrical light propagation, in physical units. A macroscopic instrument model and scene objects reflectance functions are used. SurRender is specially optimized for space scenes, with huge distances between objects and scenes up to Solar System size. Raytracing conveniently tackles some important effects for VBN algorithms: image quality, eclipses, secondary illumination, subpixel limb imaging, etc. A simulation is easily setup (in MATLAB, Python, and more) by specifying the position of the bodies (camera, Sun, planets, satellites) over time, 3D shapes and material surface properties. SurRender comes with its own modelling tool enabling to go beyond existing models for shapes, materials and sensors (projection, temporal sampling, electronics, etc.). It is natively designed to simulate different kinds of sensors (visible, LIDAR, etc.). Tools are available for manipulating huge datasets to store albedo maps and digital elevation models, or for procedural (fractal) texturing that generates high-quality images for a large range of observing distances (from millions of km to touchdown). We illustrate SurRender performances with a selection of case studies, placing particular emphasis on a 900-km Moon flyby simulation.