Point2Quad: Generating Quad Meshes from Point Clouds via Face Prediction

2025-04-28

IEEE transactions on circuits and systems for video technology (Print)

AI Summary

This work addresses the challenging problem of directly generating high-quality, pure quad meshes from raw point clouds. Methodologically, we propose the first end-to-end deep learning framework featuring: (i) a quad-aware k-NN candidate generation scheme; (ii) a dual-branch encoder—geometric (point-level) and topological (face-level)—for disentangled yet fused feature learning; and (iii) a composite loss function jointly enforcing coplanarity, convexity, and pure-quad constraints, coupled with a dedicated post-processing strategy. Our key contributions are: (1) establishing the first end-to-end mapping paradigm from unstructured point clouds to pure quad meshes; (2) introducing a dual-granularity feature disentanglement and fusion mechanism; and (3) enabling concurrent optimization of geometric quality and topological validity. Extensive experiments demonstrate that our method significantly outperforms existing baselines on both clean and noisy point clouds, achieving state-of-the-art performance in quad ratio, coplanarity, convexity, and topological correctness.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Quad meshes are essential in geometric modeling and computational mechanics. Although learning-based methods for triangle mesh demonstrate considerable advancements, quad mesh generation remains less explored due to the challenge of ensuring coplanarity, convexity, and quad-only meshes. In this paper, we present Point2Quad, the first learning-based method for quad-only mesh generation from point clouds. The key idea is learning to identify quad mesh with fused pointwise and facewise features. Specifically, Point2Quad begins with a k-NN-based candidate generation considering the coplanarity and squareness. Then, two encoders are followed to extract geometric and topological features that address the challenge of quad-related constraints, especially by combining in-depth quadrilaterals-specific characteristics. Subsequently, the extracted features are fused to train the classifier with a designed compound loss. The final results are derived after the refinement by a quad-specific post-processing. Extensive experiments on both clear and noise data demonstrate the effectiveness and superiority of Point2Quad, compared to baseline methods under comprehensive metrics.

Problem

• Generating quad-only meshes from point clouds
• Ensuring coplanarity and convexity in quad meshes
• Addressing quad-related constraints via learning-based features

Innovation

• Uses k-NN-based candidate generation for quad meshes
• Combines geometric and topological feature encoders
• Employs quad-specific post-processing refinement