用单目图像重建未知航天器3D模型，提升光照与姿态误差下的鲁棒性。
NeRF-based Spacecraft Reconstruction from Monocular Imagery Under Illumination Variability and Pose Uncertainty
- 为NeRF引入每张图的光照嵌入和姿态修正项，自适应补偿光照变化与姿态噪声。
- 在三个在轨图像集上验证，重建精度显著提升，且支持在线重建。
- 适合用于主动清除空间碎片和在轨服务等高要求任务。
在轨自主交会与近距离操作对主动清除空间碎片和在轨服务至关重要。其关键环节是利用一组2D图像离线重建目标航天器的3D模型。该任务面临两大挑战：一是轨道光照条件变化剧烈且快速；二是图像中姿态信息不准确，导致3D重建不确定性。为此，我们提出扩展神经辐射场（NeRF），引入每张图像的自由度：可学习的外观嵌入以捕捉各图像特异的光照条件，以及图像特定的姿态修正项，以优化噪声姿态标签，提升跨图像3D一致性。这些参数与NeRF联合学习，增加复杂度极小，却显著增强对光照变化和姿态误差的鲁棒性。我们在三个代表在轨操作的图像集上验证了该方法，证明其在离线重建中的有效性，并凸显其适用于在线重建这一领域开放难题。
原文摘要 · Abstract (English)
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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