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[2602.16217] Multi-Class Boundary Extraction from Implicit Representations
Summary
This paper presents a novel algorithm for multi-class boundary extraction from implicit representations, emphasizing topological correctness and adaptability in geological modeling.
Why It Matters
The study addresses a significant gap in surface extraction methods for multiple classes, which is crucial for applications in fields like computer vision and geological modeling. Ensuring topological consistency and water-tightness can enhance the accuracy of simulations and analyses in various scientific and engineering domains.
Key Takeaways
- Introduces a 2D boundary extraction algorithm for multi-class scenarios.
- Focuses on ensuring topological correctness and water-tightness.
- Allows for minimum detail constraints on approximations.
- Demonstrates adaptability using geological modeling data.
- Lays groundwork for future research in implicit representation extraction.
Computer Science > Machine Learning arXiv:2602.16217 (cs) [Submitted on 18 Feb 2026] Title:Multi-Class Boundary Extraction from Implicit Representations Authors:Jash Vira, Andrew Myers, Simon Ratcliffe View a PDF of the paper titled Multi-Class Boundary Extraction from Implicit Representations, by Jash Vira and 2 other authors View PDF HTML (experimental) Abstract:Surface extraction from implicit neural representations modelling a single class surface is a well-known task. However, there exist no surface extraction methods from an implicit representation of multiple classes that guarantee topological correctness and no holes. In this work, we lay the groundwork by introducing a 2D boundary extraction algorithm for the multi-class case focusing on topological consistency and water-tightness, which also allows for setting minimum detail restraint on the approximation. Finally, we evaluate our algorithm using geological modelling data, showcasing its adaptiveness and ability to honour complex topology. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.16217 [cs.LG] (or arXiv:2602.16217v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.16217 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jash Vira [view email] [v1] Wed, 18 Feb 2026 06:41:18 UTC (636 KB) Full-text links: Access Paper: View a PDF of the paper titled Multi-Class Boundary Extraction from Implicit Representations, by Jash Vira and 2 other authorsView PDFHTML (exper...
