GAP-GDRNet: Geometry-Aware Monocular Visual Pose Sensing on a Single-Target Synthetic Spacecraft Dataset

arXiv:2607.02360v2 Announce Type: replace-cross
Abstract: Monocular relative pose sensing is a central perception problem in non-cooperative rendezvous and on-orbit servicing. In spacecraft images, however, weak surface texture, thin appendages, illumination changes, and partial occlusion often leave only sparse and unstable geometric evidence. This article presents GAP-GDRNet, a geometry-aware attention-enhanced framework for monocular RGB-based 6D pose sensing. The method follows the geometry-guided direct regression paradigm of GDR-Net and modifies two points in the pipeline: an attention-based feature refinement (AFR) module is placed before dense geometric prediction, and a patch-level geometric self-attention (PGSA) module is inserted into Patch-PnP. AFR reinforces global spacecraft structure together with local weak-texture cues; PGSA then relates downsampled geometric patches before final pose regression. A Blender-based annotation process supplies target masks, visible-region masks, dense model-coordinate maps, camera intrinsics, and 6D pose labels for supervised training.The primary evaluation uses a single-target synthetic spacecraft dataset built from one CAD model. GAP-GDRNet reaches a rotation error of $1.96^circ$, a translation error of 0.0165 m, and an ADD@0.02 m accuracy of 95.16%, exceeding the reproduced GDR-Net baseline by 3.88 percentage points while running at 35.97 FPS for pose-network inference. Supplementary evaluations on T-LESS and LM-O give BOP AR gains of 6.8 and 3.1 percentage points over the reproduced baseline.