MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks

Qi Xuan; Fuxian Li; Yi Liu; Yun Xiang

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arXiv · 1906.06538

MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks

Qi Xuan·Fuxian Li·Yi Liu·Yun Xiang

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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.

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BibTeX RIS

Qi Xuan, Fuxian Li, Yi Liu, Yun Xiang. 2019-06-15. MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks. https://arxiv.org/abs/1906.06538

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