用机器人拍的500张图，重建国际空间站舱内高精度3D模型。
In-Situ Reconstruction of the International Space Station Using 3D Gaussian Splatting and Astrobee
- 用3D高斯点阵技术，从多视角灰度图重建空间站内部结构。
- 仅需500张图像即可生成高保真地图，渲染速度和质量优于现有方法。
- 适合航天器自主导航与舱内环境快速建图，可部署于自由飞行机器人。
本文提出一种基于3D高斯点阵（3DGS）的国际空间站（ISS）内部三维重建与建图方法。利用来自自由飞行机器人Astrobee的灰度图像数据集，构建了日本实验舱（Kibō, JEM）的完整3D点云模型。3DGS近年来在多视角场景新视图合成方面表现优异，本文首次将其应用于载人航天器内部环境建模。对比Nerfacto和TensoRF等现有方法，本方案在场景质量与渲染速度上均达到当前最优水平。实验表明，仅需500张在轨拍摄的图像，即可通过Astrobee导航相机（NavCam）实现高保真空间站内部地图重建。该成果可支持自由飞行机器人在舱内快速构建与更新地图，适用于飞船内部栖息地的自主导航与维护。
原文摘要 · Abstract (English)
This article presents a novel 3D reconstruction and mapping of the interior of the International Space Station (ISS) using 3D Gaussian Splatting (3DGS). Using existing grayscale images from the Astrobee free-flying robot dataset, we construct a full 3D splat of the ISS' Kibō or Japanese Experiment Module (JEM). 3DGS has in recent years shown promise in providing novel view synthesis of scenes captured from many images or videos, this article applies this approach to human spaceflight systems. We compare our 3DGS architecture to existing methods such as Nerfacto and TensoRF and show that reconstruction improves the state-of-the-art in both scene quality and rendering speed. We show that with as little as 500 in-situ images, a high-fidelity map can be constructed using Astrobee's Navigation Camera (NavCam) during free-flight in the JEM. These reconstructions could enable free-flyers to rapidly create and update interior maps for intra-vehicular habitats like the ISS.
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