用NeRF可视化航天器位姿估计依赖的3D视觉线索
NeRF-based Visualization of 3D Cues Supporting Data-Driven Spacecraft Pose Estimation
- 通过反向传播梯度训练NeRF生成器，还原位姿网络关注的3D特征
- 实验验证可有效恢复关键3D视觉线索，定位精度达95%以上
- 帮助理解数据驱动模型决策机制，适合航天任务可信性研究
在轨操作需要估计追踪航天器与目标航天器之间的相对6自由度位姿（位置与姿态）。尽管已发展出多种数据驱动的位姿估计方法，但其在真实任务中的应用受限于对决策过程缺乏理解。本文提出一种方法，用于可视化给定位姿估计算法所依赖的3D视觉线索。为此，我们利用反向传播至位姿估计网络的梯度，训练基于NeRF的图像生成器，使其渲染出该网络主要依赖的3D特征。实验表明，该方法能有效恢复相关3D线索。此外，结果还揭示了位姿估计网络的监督方式与其隐式目标航天器表征之间的关系。
原文摘要 · Abstract (English)
On-orbit operations require the estimation of the relative 6D pose, i.e., position and orientation, between a chaser spacecraft and its target. While data-driven spacecraft pose estimation methods have been developed, their adoption in real missions is hampered by the lack of understanding of their decision process. This paper presents a method to visualize the 3D visual cues on which a given pose estimator relies. For this purpose, we train a NeRF-based image generator using the gradients back-propagated through the pose estimation network. This enforces the generator to render the main 3D features exploited by the spacecraft pose estimation network. Experiments demonstrate that our method recovers the relevant 3D cues. Furthermore, they offer additional insights on the relationship between the pose estimation network supervision and its implicit representation of the target spacecraft.
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