NeRF-based Spacecraft Reconstruction from Monocular Imagery Under Illumination Variability and Pose Uncertainty

• URL: http://arxiv.org/abs/2605.18447v3
• Date: Wed, 20 May 2026 13:59:52 GMT
• Title: NeRF-based Spacecraft Reconstruction from Monocular Imagery Under Illumination Variability and Pose Uncertainty
• Abstract summary: A key component of such operations is the offline reconstruction of a 3D model of the target from a set of 2D images.<n>We propose to extend Neural Radiance Fields with per-image degrees of freedom to overcome these challenges.<n>We validate our approach on three image sets representative of in-orbit operations.
• Score: 14.365830773250929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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