Lightweight Geometric Compression Encoding for Additive Manufacturing

Abstract

In additive manufacturing (AM), 3D geometric models often contain some repetitive structures. To represent model topology more effectively and reduce storage space, this paper introduces a data structure using the repetition encoding framework and the hash encoding framework. First, we identify repeated meshes within the model and establish transformation groups. Then, we cluster the identical transformation groups to optimize storage space usage by utilizing the geometric relationships among facets. Finally, efficient data storage is achieved using the hash encoding framework. We devise three compatible encodings for prevalent additive manufacturing file formats based on these frameworks. We compare the encoding process with three commercial software solutions regarding the reconstruction efficiency and the storage space requirement. Experimental results exhibit that our proposed methods reduce file sizes by over 70% while preserving model topology information.