NeRF-based Simultaneous Pose Estimation and 3D Reconstruction for Non-Cooperative Space Target

Accurate pose estimation and 3D reconstruction of Non-Cooperative Space Targets (NCSTs) are critical for proximity operations in active debris removal and on-orbit servicing. In this paper, we propose a novel NeRF-based Simultaneous Pose Estimation and 3D Reconstruction (SPAR) framework to address the challenges of efficiency and reliability in traditional point-based methods, which contains three key components, \ie a multi-resolution hash encoder to reduce computational cost, a 2D keyframe feature enhancement to guide view generalization, and a direct photometric constraint to stabilize pose estimation. Furthermore, the proposed framework is evaluated on a newly constructed Spacecraft Pose Estimation and 3D Reconstruction Dataset (SPARD), comprising both synthetic and real RGB-D images and the experimental results demonstrate its effectiveness. Our framework achieves real-time processing at 51 Hz with a pose estimation accuracy of 1.26 cm translation error and 0.97° rotation error. The results show improvements over traditional and NeRF-based baselines, validating its applicability to space missions with NCST.