[논문]Pointwise CNN for 3D Object Classification on Point Cloud

Song, Wei; Liu, Zishu; Tian, Yifei; Fong, Simon

doi:10.3745/jips.02.0160

Pointwise CNN for 3D Object Classification on Point Cloud 원문보기

Journal of information processing systems, v.17 no.4, 2021년, pp.787 - 800

Song, Wei (School of Information Science and Technology, North China University of Technology) , Liu, Zishu (School of Information Science and Technology, North China University of Technology) , Tian, Yifei (Dept. of Computer and Information Science, University of Macau) , Fong, Simon (Dept. of Computer and Information Science, University of Macau)

Abstract ▼ AI-Helper

Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

주제어

표/그림 (11)

그림 Fig. 1. Proposed pointwise CNN for 3D object classification.
그림 Fig. 2. Convolution computed by 1×3 kernels.
그림 Fig. 3. Convolution computed by the 1×1 kernel.
그림 Fig. 4. Global max pooling operation.
그림 Fig. 5. Examples of categories in the ModelNet10 dataset.
그림 Fig. 6. Confusion matrix of classification results on balanced set.
표 Table 1. Partition of training sets and test sets
표 Table 2. Classification accuracy of each category on test datasets
그림 Fig. 7. Confusion matrix of classification results on unbalanced set.
표 Table 3. Classification accuracy on test set
그림 Fig. 8. Testing accuracy under different iterations.

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