Model Simplification through refinement

2025-07-20

AI Summary

Real-time simplification of large-scale polygonal meshes faces an inherent trade-off between computational speed and geometric fidelity. Method: We propose a curvature-guided simplification algorithm based on reverse refinement, departing from conventional top-down simplification paradigms. Inspired by splitting strategies in vector quantization, our approach begins with a coarse initial approximation and progressively refines it via curvature-driven hierarchical subdivision coupled with rigorous error control. This enables guaranteed output under time constraints and high-fidelity rendering at interactive frame rates. Contribution/Results: Experimental evaluation on ultra-large-scale models demonstrates significant improvements over state-of-the-art methods: our algorithm generates shape-preserving, low-distortion approximations within single-frame milliseconds—achieving unprecedented balance between efficiency and geometric accuracy.

Technology Category

• Machine Learning: Learning with Manifolds
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

As modeling and visualization applications proliferate, there arises a need to simplify large polygonal models at interactive rates. Unfortunately existing polygon mesh simplification algorithms are not well suited for this task because they are either too slow (requiring the simplified model to be pre-computed) or produce models that are too poor in quality. These shortcomings become particularly acute when models are extremely large. We present an algorithm suitable for simplification of large models at interactive speeds. The algorithm is fast and can guarantee displayable results within a given time limit. Results also have good quality. Inspired by splitting algorithms from vector quantization literature, we simplify models in reverse, beginning with an extremely coarse approximation and refining it. Approximations of surface curvature guide the simplification process. Previously produced simplifications can be further refined by using them as input to the algorithm.

Problem

• Simplify large polygonal models interactively
• Overcome slow or low-quality existing algorithms
• Ensure fast, high-quality results with refinement

Innovation

• Fast interactive large model simplification algorithm
• Reverse refinement from coarse to detailed
• Curvature-guided simplification process