Hybrid representation and adaptive multi-feature fusion for monocular 6D pose estimation in industrial assembly

Vision-based 6D pose estimation is critical for augmented reality-assisted assembly, human–robot collaboration, and quality inspection in intelligent manufacturing. However, performance degrades severely in complex industrial scenarios due to occlusion, varying lighting, textureless surfaces, and reflective parts. This work presents a monocular 6D pose estimation approach using hybrid representations and adaptive multi-feature fusion to address these challenges. A hybrid representation learning framework is designed to jointly predict keypoint heatmaps, relational vectors, semantic edges, masks, and visibility, thereby enhancing feature robustness. A multi-feature adaptive fusion strategy optimizes the pose by combining semantic and fine-grained general features. A structure-constrained correction module refines multi-object poses using assembly consistency constraints. Experiments on a custom industrial assembly dataset and the public Mono6D dataset show that the proposed method achieves 87.46% ADD (0.1d) and 86.82% 5 cm/5∘ accuracy, outperforming state-of-the-art methods. The custom dataset includes multiple weakly textured and reflective assembly parts under occlusion, lighting variation, and multi-viewpoint conditions. Furthermore, the complete system runs at approximately 18 FPS, with faster tracking once initialized. The approach supports reliable AR-assisted assembly and meets industrial deployment requirements. Our code and datasets are open-sourced at https://github.com/nengbinlv/HRMFPose, with the DOI: https://doi.org/https://doi.org/10.5281/zenodo.19574143.