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• W2950970684 abstract "As the development of deep neural networks, 3D object recognition is becoming increasingly popular in computer vision community. Many multi-view based methods are proposed to improve the category recognition accuracy. These approaches mainly rely on multi-view images which are rendered with the whole circumference. In real-world applications, however, 3D objects are mostly observed from partial viewpoints in a less range. Therefore, we propose a multi-view based 3D convolutional neural network, which takes only part of contiguous multi-view images as input and can still maintain high accuracy. Moreover, our model takes these view images as a joint variable to better learn spatially correlated features using 3D convolution and 3D max-pooling layers. Experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images which are captured from partial angles with less range. The results on 3D rotated real image dataset MIRO further demonstrate that MV-C3D is more adaptable in real-world scenarios. The classification accuracy can be further improved with the increasing number of view images." @default.
• W2950970684 created "2019-06-27" @default.
• W2950970684 creator A5008255088 @default.
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• W2950970684 date "2019-06-15" @default.
• W2950970684 modified "2023-10-18" @default.
• W2950970684 title "MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks" @default.
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• W2950970684 doi "https://doi.org/10.48550/arxiv.1906.06538" @default.
• W2950970684 hasPublicationYear "2019" @default.
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