We published a short article on a practical 6D pose estimation pipeline designed for high-mix, low-volume manufacturing, where robots must reliably handle many part variants under clutter, occlusion, and variation.
Instead of relying on a single end-to-end model, the pipeline combines classical 3D point-cloud processing and learning-based segmentation to produce stable object clusters for downstream pose estimation. The key insight is that naive clustering often fails in real manufacturing scenes, but integrating RGB segmentation makes the pipeline much more robust.
The article explains why each step matters in practice and why building with modular, composable blocks is more reliable than monolithic models. It also links to our early SDK for reusable perception and physical AI Skills and invites feedback from anyone tackling similar problems.
CCB-TK•3w ago
Instead of relying on a single end-to-end model, the pipeline combines classical 3D point-cloud processing and learning-based segmentation to produce stable object clusters for downstream pose estimation. The key insight is that naive clustering often fails in real manufacturing scenes, but integrating RGB segmentation makes the pipeline much more robust.
The article explains why each step matters in practice and why building with modular, composable blocks is more reliable than monolithic models. It also links to our early SDK for reusable perception and physical AI Skills and invites feedback from anyone tackling similar problems.