非合作空间目标的联合姿态估计与3D神经重建
Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects
AI总结：
针对非合作空间目标3D重建面临的单色图像和有限视角等挑战，提出联合优化相机位姿与NeRF的方法，通过逐张训练和旋转正则化实现了高精度重建。
摘要：
获取对地球轨道运行目标当前状态和行为的更深入了解，已被证明对于主动碎片清除、在轨维护或异常检测等一系列应用至关重要。3D模型是空间态势感知（SSA）领域中极具价值的信息源。在本研究中，我们利用神经辐射场（NeRF）基于模拟图像对非合作空间目标进行3D重建。由于不寻常的相机特性和环境条件（单色图像、未知目标朝向、有限视角、缺乏漫反射光照等），该场景对NeRF模型极具挑战性。本工作主要聚焦于相机位姿与NeRF的联合优化。实验结果表明，逐张使用连续图像进行训练能实现最精确的3D重建。我们通过优化均匀旋转来估计相机位姿，并采用正则化以防止连续位姿相差过大。
英文摘要：
Obtaining a better knowledge of the current state and behavior of objects orbiting Earth has proven to be essential for a range of applications such as active debris removal, in-orbit maintenance, or anomaly detection. 3D models represent a valuable source of information in the field of Space Situational Awareness (SSA). In this work, we leveraged Neural Radiance Fields (NeRF) to perform 3D reconstruction of non-cooperative space objects from simulated images. This scenario is challenging for NeRF models due to unusual camera characteristics and environmental conditions : mono-chromatic images, unknown object orientation, limited viewing angles, absence of diffuse lighting etc. In this work we focus primarly on the joint optimization of camera poses alongside the NeRF. Our experimental results show that the most accurate 3D reconstruction is achieved when training with successive images one-by-one. We estimate camera poses by optimizing an uniform rotation and use regularization to prevent successive poses from being too far apart.
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