基于NeRF的在轨航天器单目影像重建：在光照变化和姿态不确定性下的应用
NeRF-based Spacecraft Reconstruction from Monocular Imagery Under Illumination Variability and Pose Uncertainty
AI总结：
本文提出一种基于NeRF的方法，通过引入图像特定的外观嵌入和姿态修正项，提升在光照变化和姿态不确定性下的航天器重建鲁棒性，验证了其在离线重建中的有效性，并展示了其在在线重建中的潜力。
摘要：
自主接近和临近操作围绕非合作、未知航天器是主动碎片清除和在轨服务任务的关键。此类操作的关键组成部分是从一组2D图像中离线重建目标的3D模型。这项任务具有挑战性，因为有两个主要因素：首先，在轨光照条件表现出显著的变异性，并且随时间迅速变化。其次，图像中的姿态信息不准确，导致3D重建的不确定性。为克服这些挑战，我们提出扩展Neural Radiance Fields，引入每图像的自由度：一个可学习的外观嵌入，捕捉每张图像特定的光照条件，以及一个图像特定的姿态修正项，以细化其噪声姿态标签，提高图像间的3D一致性。这些参数增加了极小的复杂性，因为它们与NeRF联合学习，但显著提高了对光照变化和姿态不准确性的鲁棒性。我们在三个代表在轨操作的图像集中验证了我们的方法，证明了其在离线重建中的有效性，并突显了其在在线重建中的适用性，这在该领域是一个开放性问题。
英文摘要：
Autonomous rendezvous and proximity operations around uncooperative, unknown spacecraft are critical for active debris removal and on-orbit servicing missions. A key component of such operations is the offline reconstruction of a 3D model of the target from a set of 2D images. This task is challenging due to two main factors. First, in-orbit illumination conditions exhibit considerable variability, and change rapidly over time. Second, the inaccuracy of pose information in the images, results in 3D reconstruction uncertainty. To overcome these challenges, we propose to extend Neural Radiance Fields with per-image degrees of freedom: a learnable appearance embedding that captures the illumination conditions specific to each image, and an image-specific pose correction term that refines its noisy pose label to increase 3D consistency across images. These parameters add minimal complexity, as they are learned jointly with the NeRF, yet they substantially improve robustness to illumination variability and pose inaccuracies. We validate our approach on three image sets representative of in-orbit operations, demonstrating its effectiveness for offline reconstruction and highlighting its suitability for online reconstruction, an open problem in the field.
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