GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks

Shen, Yuefan; Fu, Hongbo; Du, Zhongshuo; Chen, Xiang; Burnaev, Evgeny; Zorin, Denis; Zhou, Kun; Zheng, Youyi

doi:10.1145/3480168

Cited by 26 publications

(23 citation statements)

References 58 publications

(115 reference statements)

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“…• Learning-based schemes: 1) Cascaded normal regression (CNR) (Wang, Liu, and Tong 2016), 2) Facet graph convolutions (FGC) (Armando, Franco, and Boyer 2020), 3) Normalf-net (NFN) (Li et al 2020b), 4) Nor-malNet (NNT) (Zhao et al 2021), 5) GCN-Denoiser (GCN) (Shen et al 2021). • Traditional schemes: 6) Guided normal filtering (GNF) (Zhang et al 2015), 7) Non-local low-rank normal filtering (NLF) (Li et al 2018).…”

Section: Resultsmentioning

confidence: 99%

“…These issues prevent the deployment of CNNs in mesh denoising. Accordingly, different from 2D image denoising in which CNNs-based strategy has become the basic methodology (Zhang et al 2017), to the best of our knowledge, there are only a few deep learning based schemes for mesh denoising (Wang, Liu, and Tong 2016;Li et al 2020a,b;Armando, Franco, and Boyer 2020;Zhao et al 2021;Shen et al 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, this network operates on the whole mesh, and thus is limited in the ability of local feature preservation. Similarly, in (Shen et al 2021), GCN-Denoiser is proposed very recently, which takes a triangular mesh as input and employs multiple GCNs to progressively regress the noise-free normals of the underlying surface patches. Although these methods achieve promising results, the performance of which largely relies on the power of GCNs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Local Surface Descriptor for Geometry and Feature Preserved Mesh Denoising

Zhao

Liu

Jiang

et al. 2022

AAAI

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3D meshes are widely employed to represent geometry structure of 3D shapes. Due to limitation of scanning sensor precision and other issues, meshes are inevitably affected by noise, which hampers the subsequent applications. Convolultional neural networks (CNNs) achieve great success in image processing tasks, including 2D image denoising, and have been proven to own the capacity of modeling complex features at different scales, which is also particularly useful for mesh denoising. However, due to the nature of irregular structure, CNNs-based denosing strategies cannot be trivially applied for meshes. To circumvent this limitation, in the paper, we propose the local surface descriptor (LSD), which is able to transform the local deformable surface around a face into 2D grid representation and thus facilitates the deployment of CNNs to generate denoised face normals. To verify the superiority of LSD, we directly feed LSD into the classical Resnet without any complicated network design. The extensive experimental results show that, compared to the state-of-the-arts, our method achieves encouraging performance with respect to both objective and subjective evaluations.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Local Surface Descriptor for Geometry and Feature Preserved Mesh Denoising

Zhao

Liu

Jiang

et al. 2022

AAAI

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“…Li et al [92] used cross-patch GCN with the help of CNN to perform denoising of the image, and the results show that denoising is 95% accurate. Shen et al [93] extended the denoising work using GCNs and proposed a novel approach, GCN-Denoiser, which preserves features of mesh denoising and performs graph convolution operations in the dual space of mesh triangles.…”

Section: Gcn For Computer Visionmentioning

confidence: 99%

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Bhatti

Tang

et al. 2023

International Journal of Intelligent Systems

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Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real-life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge-based graph, and other areas due to their excellent characteristics in processing non-European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph-based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.

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“…For 3D mesh denoising, Armando et al develop a multi-scale GCN in [301], where the algorithm is built on CNN-based image denoising techniques. More recently, GCN-Denoiser [302] learns a rotation-invariant graph representation for local surface patches, and performs graph convolutions over both static graph structures of local patches and dynamic learnable structures. GeoBi-GNN [303] excavates the dualgraph structure in meshes to capture both position and normal noises through a GNN-based U-Net architecture.…”

Section: D Data Denoisingmentioning

confidence: 99%

A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective

Chen¹,

Wu²,

Dai³

et al. 2022

Preprint

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Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (e.g., social network analysis and recommender systems), computer vision (e.g., object detection and point cloud learning), and natural language processing (e.g., relation extraction and sequence learning), to name a few. With the emergence of Transformers in natural language processing and computer vision, graph Transformers embed a graph structure into the Transformer architecture to overcome the limitations of local neighborhood aggregation while avoiding strict structural inductive biases. In this paper, we present a comprehensive review of GNNs and graph Transformers in computer vision from a task-oriented perspective. Specifically, we divide their applications in computer vision into five categories according to the modality of input data, i.e., 2D natural images, videos, 3D data, vision + language, and medical images. In each category, we further divide the applications according to a set of vision tasks. Such a task-oriented taxonomy allows us to examine how each task is tackled by different GNN-based approaches and how well these approaches perform. Based on the necessary preliminaries, we provide the definitions and challenges of the tasks, in-depth coverage of the representative approaches, as well as discussions regarding insights, limitations, and future directions.

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GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks

Cited by 26 publications

References 58 publications

Local Surface Descriptor for Geometry and Feature Preserved Mesh Denoising

Local Surface Descriptor for Geometry and Feature Preserved Mesh Denoising

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective

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