SubdivAR: Autoregressive Next-Scale Prediction for Neural Mesh Subdivision
SubdivAR enhances mesh subdivision accuracy via autoregressive prediction, reducing Hausdorff Distance by 18.8%.
Key Findings
Methodology
SubdivAR employs a Mesh Autoregressive Representation (MAR), treating mesh subdivision as an autoregressive next-scale prediction problem. It uses a Hybrid Topology-Aware Transformer combining global semantic attention with local feature aggregation to progressively predict vertex offsets, preserving subdivision topology while recovering details.
Key Results
- SubdivAR reduces Hausdorff Distance and Chamfer Distance by 18.8% and 14.2%, respectively, and performs well on complex open surfaces.
- On the FII-40K dataset, SubdivAR significantly outperforms existing methods, especially in handling open surfaces.
- Ablation studies show that the hybrid attention mechanism and boundary features are crucial for improving model performance.
Significance
SubdivAR is significant for academia and industry, addressing the over-smoothing issue of traditional subdivision methods and achieving breakthroughs in detail recovery. Its method can be widely applied in digital asset creation, providing higher precision in 3D model generation.
Technical Contribution
SubdivAR introduces a Mesh Autoregressive Representation and a Hybrid Topology-Aware Transformer, overcoming limitations of existing methods, offering new theoretical guarantees and engineering possibilities, especially in handling open surfaces.
Novelty
SubdivAR is the first to treat mesh subdivision as an autoregressive next-scale prediction problem, combining global and local features to significantly improve subdivision accuracy and generalization.
Limitations
- There is a domain gap between synthetic training data and real-world modeling scenarios, which may affect the recovery of extremely fine details.
- Using deterministic L2 regression may lead to over-smoothing.
Future Work
Future work could explore more advanced topology-preserving simplification algorithms for generating training data and study how to better handle the recovery of extremely fine details.
AI Executive Summary
Mesh subdivision is crucial for converting coarse meshes into high-resolution surfaces, widely used in digital asset creation. However, traditional methods often result in over-smoothing, failing to recover details. SubdivAR introduces a Mesh Autoregressive Representation (MAR), treating subdivision as an autoregressive next-scale prediction problem, combined with a Hybrid Topology-Aware Transformer, significantly improving subdivision accuracy.
In experiments, SubdivAR reduces Hausdorff Distance and Chamfer Distance by 18.8% and 14.2%, respectively, performing well on complex open surfaces. Its method significantly outperforms existing methods on the FII-40K dataset, especially in handling open surfaces. Ablation studies show that the hybrid attention mechanism and boundary features are crucial for improving model performance.
Despite its breakthroughs in subdivision accuracy, SubdivAR faces a domain gap between synthetic training data and real-world modeling scenarios, which may affect the recovery of extremely fine details. Future work could explore more advanced topology-preserving simplification algorithms for generating training data and study how to better handle the recovery of extremely fine details.
Deep Analysis
Background
Mesh subdivision is essential in 3D modeling. Traditional methods like Loop and Butterfly subdivision rules rely on fixed local refinement rules, often leading to over-smoothing. Recent neural methods like Neural Subdivision and NMR attempt to replace fixed rules with learnable operators but remain limited by local modeling, struggling to capture global semantics.
Core Problem
Traditional mesh subdivision methods perform poorly on complex geometries and open surfaces, failing to effectively recover high-frequency details. Existing neural methods also face challenges in global semantic modeling and data quality.
Innovation
SubdivAR introduces a Mesh Autoregressive Representation, treating subdivision as an autoregressive next-scale prediction problem. It uses a Hybrid Topology-Aware Transformer, combining global semantic attention with local feature aggregation, addressing limitations of existing methods.
Methodology
- �� Use Mesh Autoregressive Representation (MAR) to organize meshes at different subdivision levels.
- �� Introduce a Hybrid Topology-Aware Transformer, combining global semantic attention with local feature aggregation.
- �� Adopt a next-scale coordinate prediction paradigm, progressively predicting vertex offsets to preserve subdivision topology and recover details.
Experiments
Experiments are conducted on the FII-40K dataset, containing nearly 40,000 high-quality meshes. Performance is evaluated using Hausdorff Distance and Chamfer Distance, comparing traditional and neural subdivision methods. Ablation studies validate the importance of the hybrid attention mechanism and boundary features.
Results
SubdivAR reduces Hausdorff Distance and Chamfer Distance by 18.8% and 14.2%, respectively, performing well on complex open surfaces. Ablation studies show that the hybrid attention mechanism and boundary features are crucial for improving model performance.
Applications
SubdivAR can be widely applied in digital asset creation, particularly in scenarios requiring high-precision 3D model generation, such as animation production and game development.
Limitations & Outlook
There is a domain gap between synthetic training data and real-world modeling scenarios, which may affect the recovery of extremely fine details. Using deterministic L2 regression may lead to over-smoothing.
Plain Language Accessible to non-experts
Imagine you're building a complex castle with LEGO bricks. Traditional methods are like using regular bricks, which are stable but lack detail. SubdivAR is like a smart assistant that automatically adjusts each brick's position and shape according to your design intent, making the castle not only structurally sound but also intricately detailed. By combining global and local information, SubdivAR can maintain the overall structure while recovering every detail, making your LEGO castle more realistic and lively.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super cool 3D modeling game. You have a rough model, like a simple robot. Traditional methods are like using a ruler and scissors to trim it, but they always lack detail. SubdivAR is like a magic tool that automatically helps you make the robot super detailed, even showing every little screw clearly! It can handle not just simple models but also those complex open surfaces. Isn't that awesome?
Glossary
Mesh Subdivision
The process of converting coarse meshes into high-resolution surfaces, commonly used in 3D modeling.
Used in the paper to improve the accuracy and detail recovery of mesh subdivision.
Autoregressive Model
A predictive model where the current output depends on previous outputs.
Used to treat mesh subdivision as a next-scale prediction problem.
Transformer
A deep learning model that excels at capturing long-range dependencies in sequences.
Used to combine global semantic attention with local feature aggregation.
Hausdorff Distance
A metric measuring the maximum distance between two sets.
Used to evaluate the accuracy of mesh subdivision.
Chamfer Distance
A metric measuring the average distance between two point sets.
Used to evaluate the accuracy of mesh subdivision.
Open Questions Unanswered questions from this research
- 1 How to better generate training data that matches real-world scenarios to improve the recovery of extremely fine details.
- 2 How to further improve model stability and accuracy when handling extremely complex open surfaces.
Applications
Immediate Applications
Digital Asset Creation
SubdivAR can be used to enhance the precision and detail of 3D models, suitable for animation production and game development.
Long-term Vision
Automated 3D Modeling
SubdivAR has the potential to achieve higher levels of automated 3D modeling, reducing manual intervention and increasing production efficiency.
Abstract
Mesh subdivision is a fundamental operation for converting coarse, editable meshes into high-resolution surfaces, with broad applications in digital asset creation. Classical rule-based schemes rely on fixed local refinement rules and often produce over-smoothed surfaces. Recent neural subdivision methods improve detail synthesis, but remain constrained by local modeling and exhibit limited generalizability. We present SubdivAR, a neural mesh subdivision framework based on our proposed Mesh Autoregressive Representation (MAR). MAR arranges meshes at different subdivision levels into an ordered scale sequence, reformulating subdivision as autoregressive next-scale prediction. To support this formulation, we introduce a Hybrid Topology-Aware Transformer that combines global semantic attention with topology-constrained local feature aggregation. SubdivAR adopts a next-scale coordinate prediction paradigm, regressing vertex offsets at each refinement stage to preserve subdivision topology while recovering fine-grained geometric details. To enable reliable learning, we construct FII-40K, a curated dataset of nearly 40,000 high-quality meshes with multi-level subdivision supervision. Experiments show that SubdivAR outperforms state-of-the-art baselines, reducing Hausdorff Distance and Chamfer Distance by 18.8% and 14.2%, respectively, and demonstrates strong robustness on complex open-surface geometries.