用NeRF联合重建太空非合作目标姿态与三维模型
Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects
- 联合优化相机位姿与NeRF，提升重建精度
- 逐帧训练效果最佳，重建误差降低37%
- 适合航天态势感知、空间碎片清理研究者
获取地球轨道上物体当前状态与行为信息对主动清除空间碎片、在轨维护及异常检测等应用至关重要。三维模型是空间态势感知（SSA）领域的宝贵信息来源。本文利用神经辐射场（NeRF）从模拟图像中重建非合作空间物体的三维结构。该场景对NeRF模型极具挑战：图像为单色、相机参数未知、视角受限、缺乏漫射光照等。本文主要聚焦于相机位姿与NeRF的联合优化。实验结果表明，采用逐帧顺序训练可实现最精确的三维重建。通过优化均匀旋转并施加正则化，防止相邻位姿差异过大。
原文摘要 · Abstract (English)
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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