{"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/concurrent-binary-trees-for-large-scale-game","title":"Concurrent Binary Trees for Large-Scale Game Components","arxiv_id":"2407.02215","date":"2024-07-02","proceeding":null,"authors":["Anis Benyoub","Jonathan Dupuy"],"abstract":"A concurrent binary tree (CBT) is a GPU-friendly data-structure suitable for the generation of bisection based terrain tessellations, i.e., adaptive triangulations over square domains. In this paper, we expand the benefits of this data-structure in two respects. First, we show how to bring bisection based tessellations to arbitrary polygon meshes rather than just squares. Our approach consists of mapping a triangular subdivision primitive, which we refer to as a bisector, to each halfedge of the input mesh. These bisectors can then be subdivided adaptively to produce conforming triangulations solely based on halfedge operators. Second, we alleviate a limitation that restricted the triangulations to low subdivision levels. We do so by using the CBT as a memory pool manager rather than an implicit encoding of the triangulation as done originally. By using a CBT in this way, we concurrently allocate and/or release bisectors during adaptive subdivision using shared GPU memory. We demonstrate the benefits of our improvements by rendering planetary scale geometry out of very coarse meshes. Performance-wise, our triangulation method evaluates in less than 0.2ms on console-level hardware.","url_abs":"https://arxiv.org/abs/2407.02215v1","url_pdf":"https://arxiv.org/pdf/2407.02215v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"concurrent-binary-trees-for-large-scale-game","repo_url":"https://github.com/AnisB/large_cbt","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","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}