Mr. Maoyi Tian Profile

Maoyi Tian

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Proceedings Article | 3 October 2024 Paper

Improved multiscale features point cloud classification network based on PointNet

Qingguo Zhao, Yan Zhang, Maoyi Tian, Yizhe Fan

Proceedings Volume 13272, 132721X (2024) https://doi.org/10.1117/12.3048244

KEYWORDS: Point clouds, Data modeling, Feature extraction, Classification systems, Deep learning, Machine learning

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To address the limitations of PointNet's single-scale input, which results in incomplete feature extraction, neglect of lowand mid-level point cloud features, and feature loss caused by max pooling, an improved multi-scale feature extraction network model for point cloud classification based on PointNet is proposed. Farthest point sampling is employed to obtain point cloud inputs at different resolutions, and T-Net from PointNet is used for point cloud feature alignment. A combined multi-layer perceptron is introduced to replace the original multi-layer perceptron, enabling the extraction of both lowdimensional and medium-dimensional features. Global max pooling and global average pooling are then combined to aggregate these features. Finally, the global features obtained through mixed pooling at different scales are fused to form the final global feature vector, which is fed into a fully connected network for classification to determine the probability of each category. The proposed multi-scale feature classification network was validated on the ModelNet40 dataset. Experimental results demonstrate that an overall classification accuracy of 90.7% is achieved by our model, representing an improvement of 1.5 percentage points over PointNet, and a mean class accuracy of 86.8% is achieved, an improvement of 0.8 percentage points compared to PointNet.

Proceedings Article | 15 November 2023 Paper

A point cloud classification network with steadily increasing feature dimensions and attention mechanism

Haoyang Wang, Maoyi Tian, Wenjun Li, Yan Zhang, Jiayong Yu, Jinru Li

Proceedings Volume 12815, 128152X (2023) https://doi.org/10.1117/12.3010296

KEYWORDS: Point clouds, Feature extraction, Deep learning, Neural networks

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To solve the problem of insufficient information extraction in deep learning point cloud classification networks due to the rapid increase in dimension, as well as the issue of channel information balance leading to a lack of better discrimination ability for object features, we propose a point cloud classification network with steadily increasing feature dimensions and attention mechanism. By transforming multiple combinations of channel numbers and layers, the optimal combination of channel numbers and layers in the PointNet feature extraction module was determined, and the channel numbers were changed from (64, 64, 64, 128, 1024) to (32, 64, 128, 256, 512, 1024), resulting in a network with a feature dimension that increases steadily by a factor of 2.Incorporate the SENet channel attention mechanism into the evenly dimensionality-increased network, which weights the feature channels to enable the network to identify more accurate features and thus improve the discriminative power of object features. After training and testing on the ModelNet40 dataset, the improved classification network achieved an overall accuracy improvement of 2.58% compared to the PointNet classification network.

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