AI 中文总结
针对非合作航天器相对位姿感知中纹理弱、遮挡等问题，提出GAP-GDRNet，通过注意力特征细化与几何自注意力模块增强GDR-Net，实现鲁棒的单目6D位姿估计。
AI 中文摘要
单目相对位姿感知是非合作交会和在轨服务中的核心感知问题。然而，在航天器图像中，弱表面纹理、薄附件、光照变化和部分遮挡通常只留下稀疏且不稳定的几何证据。本文提出了GAP-GDRNet，一种用于单目RGB-based 6D位姿感知的几何感知注意力增强框架。该方法遵循GDR-Net的几何引导直接回归范式，并在流水线中修改了两点：在密集几何预测之前放置了一个基于注意力的特征细化（AFR）模块，并在Patch-PnP中插入了一个补丁级几何自注意力（PGSA）模块。AFR增强了全局航天器结构以及局部弱纹理线索；PGSA则在最终位姿回归之前关联下采样的几何补丁。基于Blender的标注过程提供了目标掩码、可见区域掩码、密集模型坐标图、相机内参和6D位姿标签，用于监督训练。
英文摘要
Monocular spacecraft 6D pose estimation remains difficult under weak texture, thin structures, illumination variation, and occlusion. This article presents GAP-GDRNet, a geometry-aware RGB framework built on GDR-Net for a single-target synthetic spacecraft benchmark. The method strengthens the geometry-guided regression pipeline at two points. First, AFR is placed before dense geometric prediction to combine global structural attention with local weak-texture enhancement. Second, PGSA is inserted into Patch-PnP to relate downsampled geometric regions before final pose regression. Dense supervision is obtained from a Blender-based rendering and annotation process that provides masks, model-coordinate maps, camera intrinsics, and 6D pose labels. On the self-built spacecraft dataset, GAP-GDRNet achieves a rotation error of 1.96°, a translation error of 0.0165 m,and 95.16% ADD@0.02 m, outperforming the reproduced GDR-Net baseline by 3.88 percentage points while running at 35.97 FPS. Tests on T-LESS and LM-O further show consistent gains over the reproduced baseline on textureless and occluded non-spacecraft objects.
