{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-to-accelerate-decomposition-for","title":"Learning to Accelerate Decomposition for Multi-Directional 3D Printing","arxiv_id":"2004.03450","date":"2020-03-17","proceeding":null,"authors":["Chen-Ming Wu","Yong-Jin Liu","Charlie C. L. Wang"],"abstract":"Multi-directional 3D printing has the capability of decreasing or eliminating the need for support structures. Recent work proposed a beam-guided search algorithm to find an optimized sequence of plane-clipping, which gives volume decomposition of a given 3D model. Different printing directions are employed in different regions to fabricate a model with tremendously less support (or even no support in many cases).To obtain optimized decomposition, a large beam width needs to be used in the search algorithm, leading to a very time-consuming computation. In this paper, we propose a learning framework that can accelerate the beam-guided search by using a smaller number of the original beam width to obtain results with similar quality. Specifically, we use the results of beam-guided search with large beam width to train a scoring function for candidate clipping planes based on six newly proposed feature metrics. With the help of these feature metrics, both the current and the sequence-dependent information are captured by the neural network to score candidates of clipping. As a result, we can achieve around 3x computational speed. We test and demonstrate our accelerated decomposition on a large dataset of models for 3D printing.","url_abs":"https://arxiv.org/abs/2004.03450v3","url_pdf":"https://arxiv.org/pdf/2004.03450v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-to-accelerate-decomposition-for","repo_url":"https://github.com/chenming-wu/pymdp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}