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UMA-Net: an unsupervised representation learning network for 3D point cloud classification

JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION (2022)

Classification of 3D terracotta warriors fragments based on geospatial and texture information

Kang Yang et al.

Summary: A novel classification framework for 3D Terracotta Warriors fragments is proposed in this paper, achieving an average accuracy rate of 91.41% by integrating geospatial and texture information with a dual-modal neural network, outperforming existing methods.

JOURNAL OF VISUALIZATION (2021)

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Article Environmental Sciences

AMS-Net: An Attention-Based Multi-Scale Network for Classification of 3D Terracotta Warrior Fragments

Jie Liu et al.

Summary: AMS-Net is an attention-based multi-scale neural network proposed for extracting significant geometric and semantic features of Terracotta Warrior fragments for effective classification. With a multi-scale strategy, it can extract local and global features from different scales in parallel, enhancing accuracy and performance in classification tasks.

REMOTE SENSING (2021)

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Article Optics

Simplification method for 3D Terracotta Warrior fragments based on local structure and deep neural networks

Guohua Geng et al.

JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION (2020)

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Article Engineering, Electrical & Electronic

Classification of Cultural Fragments Based on Adaptive Weights of Multi-Feature Descriptions

Lu Zhengjie et al.

LASER & OPTOELECTRONICS PROGRESS (2020)

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Article Engineering, Biomedical

Environmental Features Recognition for Lower Limb Prostheses Toward Predictive Walking

Kuangen Zhang et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2019)

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Article Computer Science, Software Engineering

Dynamic Graph CNN for Learning on Point Clouds

Yue Wang et al.

ACM TRANSACTIONS ON GRAPHICS (2019)

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Article Computer Science, Information Systems

Cassifying fragments of terracotta warriors using template-based partial matching

Guoguang Du et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2018)

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UMA-Net: an unsupervised representation learning network for 3D point cloud classification

相关参考文献

Classification of 3D terracotta warriors fragments based on geospatial and texture information

AMS-Net: An Attention-Based Multi-Scale Network for Classification of 3D Terracotta Warrior Fragments

Simplification method for 3D Terracotta Warrior fragments based on local structure and deep neural networks

Classification of Cultural Fragments Based on Adaptive Weights of Multi-Feature Descriptions

Environmental Features Recognition for Lower Limb Prostheses Toward Predictive Walking

Dynamic Graph CNN for Learning on Point Clouds

Cassifying fragments of terracotta warriors using template-based partial matching

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