Asteroid-GS: 3D Gaussian splatting for fast surface reconstruction of asteroids

¹ School of Astronautics, Beihang University, Beijing 100191, China
² State Key Laboratory of High-Efficiency Reusable Aerospace Transportation Technology, Beijing 102206, China

^★ Corresponding authors: cuily@buaa.edu.cn

Received: 4 August 2025
Accepted: 6 November 2025

Abstract

Context. Asteroid surface reconstruction is essential for deep space exploration missions, as it provides critical information about surface morphology that supports spacecraft navigation and sample acquisition. Traditional methods, such as stereo-photogrammetry (SPG) and stereo-photoclinometry (SPC), have been widely applied in asteroid missions, which often rely on large amounts of data or considerable manual intervention to derive reliable models. Meanwhile, intelligent methods based on the neural radiance field (NeRF) suffer from slow processing speeds, often requiring several hours or even days to complete surface reconstruction. Recent 3D Gaussian splatting (3DGS) shows promise in fast surface reconstruction but faces some challenges in asteroid scenarios, limiting its direct application.

Aims. This paper presents Asteroid-GS, a fast and intelligent method for reconstructing asteroid surface models based on 3DGS. It is intended to complement current methodologies, enabling asteroid reconstruction with a limited number of images and a small amount of processing time while achieving an accuracy comparable that of to existing algorithms.

Methods. Our method incorporates an adaptive Gaussian pruning strategy to remove noise from asteroids in deep space environments. The shallow multilayer perceptrons integrated with asteroid illumination are employed to improve the reconstruction in both well-lit and shadowed regions. Additionally, we employ geometric regularization techniques to enhance surface detail preservation and construct the Gaussian opacity field to enable accurate surface mesh extraction.

Results. Experimental results on asteroids Itokawa and Ryugu demonstrate that our method outperforms state-of-the-art 3DGS-based methods in terms of 3D model accuracy and novel view synthesis. It maintains geometric consistency with traditional models, achieving better results than SPG given the same input images, while notably reducing processing time and manual intervention compared to SPC. Asteroid-GS completes reconstruction within one hour, requiring significantly less time than NeRF-based methods. Our work provides a supplementary solution for asteroid surface reconstruction, potentially improving the efficiency of future exploration missions.