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A data-centric unsupervised 3d mesh segmentation method

VISUAL COMPUTER 2024年第4期40卷 2237-2249页

作者： Sivri, Talya Tumer Sahillioglu, Yusuf METU Multimedia Informat Dept Ankara Turkiye METU Comp Engn Dept Ankara Turkiye

In this paper, a novel data-centric approach is proposed for solving the 3d mesh segmentation problem. The method uses node2vec, a semi-supervised learning algorithm, to create vector embedding representations for each node in a 3d mesh graph. This makes the mesh data more compact and easier to process which is important for reducing computation costs. K-Means clustering is then used to cluster each node according to their node embedding information. This data-centric approach is more computationally efficient than other complex models such as CNN and RNN. The main contribution of this study is the development of a data-centric AI framework that combines node2vec embedding, machine learning, and deep learning techniques. The use of cosine similarity is also adapted to compare and evaluate the trained node embedding vectors with different hyperparameters. Additionally, a new algorithm is developed to determine the optimal cluster number using geodesic distance on the 3d mesh. Overall, this approach provides competitive results compared to existing mesh segmentation methods.

关键词： 3d mesh segmentation Unsupervised learning Embedding Node2vec K-Means Geodesic distance

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AdA-SCMS Net: A self-supervised clustering-based 3d mesh segmentation network with aggregation dual autoencoder

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COMPUTERS & GRAPHICS-UK 2024年 124卷

作者： Jiao, Xue Yang, Xiaohui Henan Univ Henan Engn Res Ctr Artificial Intelligence Theory Sch Math & Stat Kaifeng 475000 Peoples R China Henan Inst Sci & Technol Sch Math Xinxiang 453003 Peoples R China

despite significant advances in 3d mesh segmentation techniques driven by deep learning, segmenting 3d meshes without exhaustive manual labeling remains a challenging due to difficulties in acquiring high-quality labeled datasets. This paper introduces an a ggregation d ual a utoencoder s elf-supervised c lustering-based m esh s egmentation network for unlabeled 3d meshes (AdA-SCMS Net). Expanding upon the previously proposed SCMS-Net, the AdA-SCMS Net enhances the segmentation process by incorporating a denoising autoencoder with an improved graph autoencoder as its basic structure. This modification prompts the segmentation network to concentrate on the primary structure of the input data during training, enabling the capture of robust features. In addition, the AdA-SCMS network introduces two new modules. One module is named the branch aggregation module, which combines the strengths of two branches to create a semantic latent representation. The other is the aggregation self-supervised clustering module, which facilitates end-to-end clustering training by iteratively updating each branch through mutual supervision. Extensive experiments on benchmark datasets validate the effectiveness of the AdA-SCMS network, demonstrating superior segmentation performance compared to the SCMS network.

关键词： 3d mesh segmentation Unsupervised segmentation Self-supervised learning Clustering

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A Hybrid CNN-CRF Inference Models for 3d mesh segmentation 6

A Hybrid CNN-CRF Inference Models for 3D Mesh Segmentation

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6th IEEE International Congress on Information Science and Technology (IEEE CiSt)

作者： Abouqora, Youness Herouane, Omar Moumoun, Lahcen Gadi, Taoufiq Hassan 1st Univ Fac Sci & Tech Math Comp Sci & Engn Sci Lab MISI Settat Morocco

ISBN: (纸本)9781728166469

Owing to the wide spread of the 3d objects technologies, learning 3d objects labeling and segmentation is becoming one of the most provocative tasks in computer vision and digital multimedia. Recently, convolutional neural network (CNN) has proved itself as a powerful model in segmentation and classification by giving excellent performances. The use of a graphical model such as conditional random field (CRF) contributes further in capturing contextual information and thus improving the segmentation performance. In this paper, we implement a deep architecture in order to segment and label 3d shape parts by considering both spectral and geometric features via a combined framework consisting of a CNN and CRF models. First, low-level features are used to learn deep features using a CNN model, and then formulate the deep CRF model with a CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between adjacent triangles on the mesh. By comparing our results with those from several state-of-the-art, our method shows promising potentials.

关键词： 3d mesh segmentation Surface based Features Convolutional Neural Network Conditional Random Field Accuracy metrics

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Pose-independent efficient gauge equivariant network for 3d mesh aneurysm segmentation

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NEUROCOMPUTING 2025年 639卷

作者： Ru, Xudong Wang, Xingce Liu, Zhenhong du, Peng Zhao, Haichuan Wu, Zhongke Ju, Xiaodong Liu, Shaolong Zhu, Yi-Cheng Frangi, Alejandro F. Beijing Normal Univ Sch Artificial Intelligence 19 Xinjiekouwai St Beijing 100875 Peoples R China Peking Univ Third Hosp Dept Sports Med 49 North Garden Rd Beijing 100191 Peoples R China Peking Union Med Coll Hosp 1 Shuaifuyuan Wangfujing Dongcheng Dist Beijing 100730 Peoples R China Univ Manchester Geog Dept Oxford Rd Manchester M13 9PL England Katholieke Univ Leuven Oude Markt 13 B-3000 Leuven Belgium Alan Turing Inst British Lib 96 Euston Rd London NW1 2DB England

Accurate detection and segmentation of intracranial aneurysms (IAs) in three-dimensional medical imaging are crucial for effective diagnosis and therapeutic planning, given the complex nature of cerebral vascular structures. These aneurysms present significant challenges due to their variable sizes, unpredictable locations, diverse poses, and morphological heterogeneity, making consistent and reliable segmentation a critical yet demanding task in medical imaging. To effectively tackle these challenges, this study leverages the principles of anisotropic gauge equivariant convolutions (GEC) to propose a comprehensive framework that encompasses three key innovations: a Pose-Independent feature Representation (PIR) module, an Intra-layer Recurrent Convolutional (IRC) module and a boundary enhanced Laplacian loss. We introduce a novel PIR module, that utilizes anisotropic GEC to handle the inherent geometric complexities that independently of their poses. Our network, featuring a IRC module, strategically reuses convolutional weights to extend the global receptive field without additional parameters, thereby maintaining efficient high-resolution feature learning across mesh surfaces. Additionally, we incorporate a novel Laplacian loss that enforces boundary sharpness, greatly enhancing the delineation of aneurysm boundaries. The proposed method achieves superior segmentation accuracy, clearly delineating aneurysm necks without extensive data augmentation. Experiments on two datasets (IntrA and IntrANeurIST) verify the validity of the proposed method by outperforming the state of the art method together with ablation study. The method can be easily extended in the similar aneurysm segmentation such as abdominal aortic aneurysm and ophthalmic aneurysms.

关键词： Intracranial aneurysm segmentation Geometric deep learning 3d mesh segmentation Gauge equivariant convolution Intra-layer recurrent convolution Laplacian loss

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3d mesh segmentation Based on Unsupervised Clustering 2nd

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2nd International Conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Khattab, dina Ebeid, Hala M. Hussein, Ashraf S. Tolba, Mohamed F. Ain Shams Univ Fac Comp & Informat Sci Cairo 11566 Egypt Arab Open Univ Fac Comp Studies Kuwait 13033 Kuwait

ISBN: (纸本)9783319483085;9783319483078

3d mesh segmentation is considered an important process in the field of computer graphics. It is a fundamental process in different applications such as shape reconstruction in reverse engineering, 3d models retrieval, and CAd/CAM applications, etc. It consists of subdividing a polygonal surface into patches of uniform properties either from a geometrical point of view or from a perceptual/semantic point of view. In this paper, unsupervised clustering techniques for the 3d mesh segmentation problem are introduced. The K-means and the Fuzzy C-means (FCM) clustering techniques are selected for the development of the proposed clustering-based 3d mesh segmentation techniques. Since the mesh faces are considered the main element, the clustering technique is applied to the dual mesh. The 3d Euclidean distance is used as the distance measure to compute matching between mesh elements. Based on empirical results on a benchmark dataset of 3d mesh models, the FCM-based mesh segmentation technique outperforms the K-means-based one in terms of accuracy and consistency with human segmentations.

关键词： 3d mesh segmentation Unsupervised clustering K-means FCM

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A comprehensive overview of methodologies and performance evaluation frameworks in 3d mesh segmentation

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COMPUTER VISION ANd IMAGE UNdERSTANdING 2015年 135卷 49-82页

作者： Theologou, Panagiotis Pratikakis, Loannis Theoharis, Theoharis Democritus Univ Thrace Dept Elect & Comp Engn GR-67100 Xanthi Greece NTNU Dept Comp & Informat Sci Trondheim Norway Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens Greece

3d mesh segmentation has become a crucial part of many applications in 3d shape analysis. In this paper, a comprehensive survey on 3d mesh segmentation methods is presented. Analysis of the existing methodologies is addressed taking into account a new categorization along with the performance evaluation frameworks which aim to support meaningful benchmarks not only qualitatively but also in a quantitative manner. This survey aims to capture the essence of current trends in 3d mesh segmentation. (C) 2015 Elsevier Inc. All rights reserved.

关键词： 3d mesh segmentation 3d shape analysis 3d object representation

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Supervertex Sampling Network: A Geodesic differential SLIC Approach for 3d mesh

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IEEE TRANSACTIONS ON VISUALIZATION ANd COMPUTER GRAPHICS 2024年第8期30卷 5553-5565页

作者： Zhuang, Jiafu Zeng, Pan Zhuang, Wei Guo, Xiaoyu Liu, Peizhong Quanzhou Normal Univ Sch Phys & Informat Engn Quanzhou 362000 Fujian Peoples R China Huaqiao Univ Sch Med Quanzhou 362021 Fujian Peoples R China Univ Manchester Dept Social Stat Manchester M13 9PL England Quanzhou Normal Univ Coll Oceanol & Food Sci Quanzhou 362000 Fujian Peoples R China Huaqiao Univ Coll Engn Quanzhou 362021 Fujian Peoples R China Quanzhou Med Coll Sch Med Quanzhou 362021 Fujian Peoples R China

The analysis of 3d meshes with deep learning has become prevalent in computer graphics. As an essential structure, hierarchical representation is critical for mesh pooling in multiscale analysis. Existing clustering-based mesh hierarchy construction methods involve nonlinear discretization optimization operations, making them nondifferential and challenging to embed in other trainable networks for learning. Inspired by deep superpixel learning methods in image processing, we extend them from 2d images to 3d meshes by proposing a novel differentiable chart-based segmentation method named geodesic differential supervertex (GdSV). The key to the GdSV method is to ensure that the geodesic position updates are differentiable while satisfying the constraint that the renewed supervertices lie on the manifold surface. To this end, in addition to using the differential SLIC clustering algorithm to update the nonpositional features of the supervertices, a reparameterization trick, the Gumbel-Softmax trick, is employed to renew the geodesic positions of the supervertices. Therefore, the geodesic position update problem is converted into a linear matrix multiplication issue. The GdSV method can be an independent module for chart-based segmentation tasks. Meanwhile, it can be combined with the front-end feature learning network and the back-end task-specific network as a plug-in-plug-out module for training;and be applied to tasks such as shape classification, part segmentation, and 3d scene understanding. Experimental results show the excellent performance of our proposed algorithm on a range of datasets.

关键词： Three-dimensional displays Clustering algorithms Shape Task analysis Manifolds deep learning Training 3d mesh segmentation 3d shape learning deep clustering graph neural network

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MeT: A graph transformer for semantic segmentation of 3d meshes

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COMPUTER VISION ANd IMAGE UNdERSTANdING 2023年第1期235卷

作者： Vecchio, Giuseppe Prezzavento, Luca Pino, Carmelo Rundo, Francesco Palazzo, Simone Spampinato, Concetto Univ Catania Dept Comp Engn Viale Andrea Doria 6 I-95127 Catania Italy STMicroelectron ADG R&D Power & Discretes Str Primosole 50 I-95121 Catania Italy

Polygonal meshes have become the standard for discretely approximating 3d shapes, thanks to their efficiency and high flexibility in capturing non-uniform shapes. This non-uniformity, however, leads to irregularity in the mesh structure, making tasks like segmentation of 3d meshes particularly challenging. Semantic segmentation of 3d mesh has been typically addressed through CNN-based approaches, leading to good accuracy. Recently, transformers have gained enough momentum both in NLP and computer vision fields, achieving performance at least on par with CNN models, supporting the long-sought architecture universalism. Following this trend, we propose a transformer-based method for semantic segmentation of 3d mesh motivated by a better modeling of the graph structure of meshes, by means of global attention mechanisms. In order to address the limitations of standard transformer architectures in modeling relative positions of non-sequential data, as in the case of 3d meshes, as well as in capturing the local context, we perform positional encoding by means the Laplacian eigenvectors of the adjacency matrix, replacing the traditional sinusoidal positional encodings, and by introducing clustering-based features into the self-attention and cross-attention operators. Experimental results, carried out on three sets of the Shape COSEG dataset (Wang et al., 2012), on the human segmentation dataset proposed in Maron et al. (2017) and on the ShapeNet benchmark (Chang et al., 2015), show how the proposed approach yields state-of-the-art performance on semantic segmentation of 3d meshes.

关键词： 3d mesh segmentation Graph neural networks Transformers 3d meshes segmentation

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SCMS-Net: Self-Supervised Clustering-Based 3d meshes segmentation Network

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COMPUTER-AIdEd dESIGN 2023年 160卷

作者： Jiao, Xue Chen, Yonggang Yang, Xiaohui Henan Inst Sci & Technol Sch Math Xinxiang 453003 Peoples R China Henan Univ Henan Engn Res Ctr Artificial Intelligence Theory Sch Math & Stat Kaifeng 475000 Peoples R China

The superior performance of deep learning in different domains has sparked significant interest in its applicability to 3d computer graphics. deep learning has become the dominant technical architecture in current 3d mesh segmentation. However, learning-based 3d segmentation methods usually rely on high-quality training datasets, which are not readily available in practical applications. How to segment 3d meshes without exhaustive label annotations remains a challenging problem, especially in the context of deep learning. As a subset of unsupervised learning methods, self-supervised learning offers a promising learning paradigm for unlabeled 3d mesh segmentation. In this paper, we introduce a self-supervised clustering-Based network specifically for the segmentation of label-free 3d meshes. Our self-supervised clustering-based 3d mesh segmentation network (SCMS-Net) employs a two-branch architecture to learn effective feature representation. The two branches are unified into an end-to-end framework using a self-supervised strategy. Finally, the label predictions of the parts are generated by iterative clustering. We conducted ablation studies and comparative experiments on a standard benchmark to demonstrate the effectiveness of our approach. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： 3d mesh segmentation Unsupervised segmentation Self-supervised learning deep clustering

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Vector-Aware Anisotropic Gauge Equivariant mesh Convolution Network for 3d Aneurysm detection 24

Vector-Aware Anisotropic Gauge Equivariant Mesh Convolution ...

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4th Annual International Conference on Multimedia Retrieval (ICMR)

作者： Ru, Xudong Zhao, Haichuan Wang, Xingce Wu, Zhongke Liu, Shaolong Zhu, Yi-Cheng Frangi, Alejandro F. Beijing Normal Univ Beijing Peoples R China Peking Union Med Coll Hosp Beijing Peoples R China Univ Manchester Manchester England Katholieke Univ Leuven Leuven Belgium Alan Turing Inst London England

ISBN: (纸本)9798400706028

Automatic detecting intracranial aneurysms (IAs) poses significant challenges due to their diversity, varying locations, and complex classifications by size, shape, and phenotype. Current shape-based IAs detection methods, while promising, often neglect the topological connectivity of IAs vertices and the variable traits of the aneurysm's neck, leading to fragmented detections. To address these issues, we present a mesh convolutional neural network based on gauge equivariant convolution to leverage the topological and geometric features of 3d mesh models. Our network comprises four key components: anisotropic message passing (AMP) on mesh surfaces, gauge equivariant convolution (GEC), vector-aware feature reconstruction (VFR), and a pooling-free convolutional architecture. AMP ensures accurate detection of IAs from surrounding vessels by utilizing topological connectivity and anisotropic relationships between mesh vertices. GEC offers rotational equivariance for consistently learning geometric features, improving feature learning stability and efficiency. VFR preserves the geometric and directional integrity of the vector features, enriching the representational capacity of the network. The pooling-free convolutional architecture captures local and global geometric nuances of 3d meshes, achieving precise IAs detection and producing sharper IAs boundaries. Tests on the IntrA dataset show our method outperforms the current best by 1.83% and 1.02% in mIoU and mdSC, respectively.

关键词： intracranial aneurysm detection 3d mesh segmentation gauge equivariant convolution vector neural network

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