Near-convex decomposition and layering for efficient 3D printing

We introduce a novel divide-and-conquer approach for 3D printing, which provides automatic decomposition and configuration of an input object into print-ready components. Our method improves 3D printing by reducing material consumption, decreasing printing time, and improving fidelity of printed models. An input object is decomposed into a set of components obtained by a near-convex segmentation that minimizes an energy function. Then the configuration phase provides a robust algorithm to pack the components for an efficient print job. Our approach has been tested on both simulated models and real-world printed objects. Our results show that the framework can reduce print time by up to 65% (fused deposition modeling, or FDM) and 36% (stereolithography, or SLA) on average and diminish material consumption by up to 35% (FDM) and 10% (SLA) on consumer printers, while also providing more accurate objects.