用双流图网络和特征引导变换器，高效去除3D网格噪声。
DMD-Net: Deep Mesh Denoising Network
- 双流结构在原始与对偶图上同步聚合信息，提升细节保留能力。
- 在大规模数据集上训练，对高噪声场景仍保持优异去噪效果。
- 适合需要高质量3D重建的工业设计与医疗建模场景。
我们提出深度网格去噪网络（DMD-Net），一种端到端的深度学习框架，用于解决网格去噪问题。DMD-Net包含一个图卷积神经网络，其在原始图和对偶图上均进行信息聚合，通过异构双流结构实现，并引入原始-对偶融合模块促进两路间通信。我们设计了特征引导变换器（FGT）范式，包含特征提取器、变换器和去噪器。特征提取器估计局部特征，引导变换器对噪声输入网格进行变换，生成有用中间表示，再由去噪器输出去噪后的网格。网络在大规模3D物体数据集上训练，经充分消融实验验证各组件对性能至关重要。相比现有先进算法，本方法在多种噪声类型下表现相当或更优，且在极端高噪声条件下仍具出色鲁棒性。
原文摘要 · Abstract (English)
We present Deep Mesh Denoising Network (DMD-Net), an end-to-end deep learning framework, for solving the mesh denoising problem. DMD-Net consists of a Graph Convolutional Neural Network in which aggregation is performed in both the primal as well as the dual graph. This is realized in the form of an asymmetric two-stream network, which contains a primal-dual fusion block that enables communication between the primal-stream and the dual-stream. We develop a Feature Guided Transformer (FGT) paradigm, which consists of a feature extractor, a transformer, and a denoiser. The feature extractor estimates the local features, that guide the transformer to compute a transformation, which is applied to the noisy input mesh to obtain a useful intermediate representation. This is further processed by the denoiser to obtain the denoised mesh. Our network is trained on a large scale dataset of 3D objects. We perform exhaustive ablation studies to demonstrate that each component in our network is essential for obtaining the best performance. We show that our method obtains competitive or better results when compared with the state-of-the-art mesh denoising algorithms. We demonstrate that our method is robust to various kinds of noise. We observe that even in the presence of extremely high noise, our method achieves excellent performance.
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