Local-to-global viewpoint selection based on geometric models for robotic 3D object scanning

Three-dimensional surface measurement of complex objects is critical for industrial component manufacturing, inspection, and sorting. Robotic arm-mounted 3D scanning systems exhibit remarkable advantages in efficiency and repeatability, while requiring effective global viewpoint selection before path planning. In this paper, we propose a primitive-based local-to-global viewpoint selection method for offline viewpoint planning of complex objects with known models. Our two-stage framework consists of bottom-up local viewpoint generation and top-down global optimization. Firstly, an initial viewpoint set is generated for surface facets. We propose a local viewpoint refinement method that groups neighboring facets into a local primitive, thereby optimizing the initial viewpoint through geometry-aware weighted adjustment of its position and orientation. Then, we establish a bidirectional viewpoint-primitive coverage relationship for global optimization, yielding a compact viewpoint set while preserving scanning coverage. By leveraging neighboring geometry and mutual coverage, our method improves viewpoint compactness and surface coverage. Unlike next-best-view methods, we focus on offline global viewpoint selection rather than online dynamic planning. Experiments on real 3D objects show our method outperforms selected representative baselines, achieving 97.81% coverage with fewer number of viewpoints, and maintains higher computational efficiency of 38.98 seconds.