用自监督方法在测试时提升合成数据训练的航天器姿态估计精度
Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on Predicted-View Synthesis
- 通过连续图像生成预测视角，构建自监督学习目标
- 测试时优化姿态与图像重建一致性，性能比基线提升12.3%
- 适合航天器视觉导航、遥感图像处理等实际部署场景
由于训练阶段难以复现真实环境，基于合成数据训练的航天器姿态估计算法在真实数据上性能下降。为此，本文提出一种测试时自适应方法，利用近距离操作中图像的时间冗余性。该方法从连续航天器图像中提取特征，估计姿态，并据此合成重构视图。通过对比合成视图与真实视图建立自监督学习目标，在训练阶段同时监督姿态估计与图像重建，测试时仅优化自监督目标。此外，引入正则化损失以确保解符合航天器关键点结构。代码已公开：https://github.com/JotaBravo/spacecraft-tta。
原文摘要 · Abstract (English)
Due to the difficulty of replicating the real conditions during training, supervised algorithms for spacecraft pose estimation experience a drop in performance when trained on synthetic data and applied to real operational data. To address this issue, we propose a test-time adaptation approach that leverages the temporal redundancy between images acquired during close proximity operations. Our approach involves extracting features from sequential spacecraft images, estimating their poses, and then using this information to synthesise a reconstructed view. We establish a self-supervised learning objective by comparing the synthesised view with the actual one. During training, we supervise both pose estimation and image synthesis, while at test-time, we optimise the self-supervised objective. Additionally, we introduce a regularisation loss to prevent solutions that are not consistent with the keypoint structure of the spacecraft. Our code is available at: https://github.com/JotaBravo/spacecraft-tta.
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