A 3D Point Cloud Classification Method Based on Adaptive Graph Convolution and Global Attention

Yue, Yaowei; Li, Xiaonan; Peng, Yun

doi:10.3390/s24020617

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…With the development of three-dimensional (3D) sensor technology, significant strides in point cloud capturing and reconstruction have spurred a surge in interest in 3D media applications, including virtual reality, immersive telepresence, and free-viewpoint television [1][2][3]. Point clouds are widely adopted for representing both static and dynamic objects in 3D space.…”

Section: Introductionmentioning

confidence: 99%

Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression

Shao,

Gao,

Liu

et al. 2024

Sensors

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The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme.

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Section: Introductionmentioning

confidence: 99%

Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression

Shao,

Gao,

Liu

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The advancement of three-dimensional (3D) sensing and acquisition technologies has elevated point clouds as a prevalent media format for representing 3D objects and scenes across various multimedia applications [1][2][3][4]. A point cloud consists of a set of points distributed in 3D space, with each point possessing geometry positions and attribute information, such as color, intensity, reflectance, etc.…”

Section: Introductionmentioning

confidence: 99%

Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression

Shao,

Song,

Gao

et al. 2024

Applied Sciences

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There is a pressing need across various applications for efficiently compressing point clouds. While the Moving Picture Experts Group introduced the geometry-based point cloud compression (G-PCC) standard, its attribute compression scheme falls short of eliminating signal frequency-domain redundancy. This paper proposes a texture-guided graph transform optimization scheme for point cloud attribute compression. We formulate the attribute transform coding task as a graph optimization problem, considering both the decorrelation capability of the graph transform and the sparsity of the optimized graph within a tailored joint optimization framework. First, the point cloud is reorganized and segmented into local clusters using a Hilbert-based scheme, enhancing spatial correlation preservation. Second, the inter-cluster attribute prediction and intra-cluster prediction are conducted on local clusters to remove spatial redundancy and extract texture priors. Third, the underlying graph structure in each cluster is constructed in a joint rate–distortion–sparsity optimization process, guided by geometry structure and texture priors to achieve optimal coding performance. Finally, point cloud attributes are efficiently compressed with the optimized graph transform. Experimental results show the proposed scheme outperforms the state of the art with significant BD-BR gains, surpassing G-PCC by 31.02%, 30.71%, and 32.14% in BD-BR gains for Y, U, and V components, respectively. Subjective evaluation of the attribute reconstruction quality further validates the superiority of our scheme.

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Graph Neural Networks in Point Clouds: A Survey

Li,

Lu,

Chen

et al. 2024

Remote Sensing

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With the advancement of 3D sensing technologies, point clouds are gradually becoming the main type of data representation in applications such as autonomous driving, robotics, and augmented reality. Nevertheless, the irregularity inherent in point clouds presents numerous challenges for traditional deep learning frameworks. Graph neural networks (GNNs) have demonstrated their tremendous potential in processing graph-structured data and are widely applied in various domains including social media data analysis, molecular structure calculation, and computer vision. GNNs, with their capability to handle non-Euclidean data, offer a novel approach for addressing these challenges. Additionally, drawing inspiration from the achievements of transformers in natural language processing, graph transformers have propelled models towards global awareness, overcoming the limitations of local aggregation mechanisms inherent in early GNN architectures. This paper provides a comprehensive review of GNNs and graph-based methods in point cloud applications, adopting a task-oriented perspective to analyze this field. We categorize GNN methods for point clouds based on fundamental tasks, such as segmentation, classification, object detection, registration, and other related tasks. For each category, we summarize the existing mainstream methods, conduct a comprehensive analysis of their performance on various datasets, and discuss the development trends and future prospects of graph-based methods.

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A 3D Point Cloud Classification Method Based on Adaptive Graph Convolution and Global Attention

Cited by 3 publications

References 34 publications

Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression

Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression

Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression

Graph Neural Networks in Point Clouds: A Survey

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