Structure-Prior-Guided Point Cloud Completion for Industrial Mechanical Components

Point cloud completion for industrial mechanical components remains challenging due to reflections, self-occlusions, sparse sampling, and strict takt-time constraints in production, which often lead to large missing regions and incomplete fine structures. Meanwhile, industrial parts exhibit strong geometric regularities and prominent sharp features, making purely global feature-driven completion prone to structural drift and blurred boundaries. To address these issues, we propose a structure-prior-guided point cloud completion framework for industrial workpieces. Our method follows an encoder–decoder design with a coarse-to-fine generation strategy to balance global consistency and local geometric details. It enhances feature representation via local graph enhancement and relative-position attention, and further injects a primitive decomposition prior from ParSeNet into progressive decoding to condition point generation and displacement refinement. Experiments on industrial CAD datasets such as CADNET demonstrate that our approach achieves higher geometric fidelity and structural integrity under varying occlusion conditions, and also yields superior performance in downstream surface reconstruction evaluation compared with existing methods.