{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scientific-image-rendering-for-space-scenes","title":"Scientific image rendering for space scenes with the SurRender software","arxiv_id":"1810.01423","date":"2018-10-02","proceeding":null,"authors":["Roland Brochard","Jérémy Lebreton","Cyril Robin","Keyvan Kanani","Grégory Jonniaux","Aurore Masson","Noela Despré","Ahmad Berjaoui"],"abstract":"Spacecraft autonomy can be enhanced by vision-based navigation (VBN)\ntechniques. Applications range from manoeuvers around Solar System objects and\nlanding on planetary surfaces, to in-orbit servicing or space debris removal.\nThe development and validation of VBN algorithms relies on the availability of\nphysically accurate relevant images. Yet archival data from past missions can\nrarely serve this purpose and acquiring new data is often costly. The SurRender\nsoftware is an image simulator that addresses the challenges of realistic image\nrendering, with high representativeness for space scenes. Images are rendered\nby raytracing, which implements the physical principles of geometrical light\npropagation, in physical units. A macroscopic instrument model and scene\nobjects reflectance functions are used. SurRender is specially optimized for\nspace scenes, with huge distances between objects and scenes up to Solar System\nsize. Raytracing conveniently tackles some important effects for VBN\nalgorithms: image quality, eclipses, secondary illumination, subpixel limb\nimaging, etc. A simulation is easily setup (in MATLAB, Python, and more) by\nspecifying the position of the bodies (camera, Sun, planets, satellites) over\ntime, 3D shapes and material surface properties. SurRender comes with its own\nmodelling tool enabling to go beyond existing models for shapes, materials and\nsensors (projection, temporal sampling, electronics, etc.). It is natively\ndesigned to simulate different kinds of sensors (visible, LIDAR, etc.). Tools\nare available for manipulating huge datasets to store albedo maps and digital\nelevation models, or for procedural (fractal) texturing that generates\nhigh-quality images for a large range of observing distances (from millions of\nkm to touchdown). We illustrate SurRender performances with a selection of case\nstudies, placing particular emphasis on a 900-km Moon flyby simulation.","url_abs":"http://arxiv.org/abs/1810.01423v1","url_pdf":"http://arxiv.org/pdf/1810.01423v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"scientific-image-rendering-for-space-scenes","repo_url":"https://github.com/SurRenderSoftware/surrender_client_API","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"scientific-image-rendering-for-space-scenes","repo_url":"https://github.com/SurRenderSoftware/surrender_scenarios","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}