三维形状识别是近年来较为热门的研究方向,针对其中的三维模型形状的表达方法和识别问题,提出一种多分支卷积神经网络下的三维模型识别方法.该方法通过对三维模型进行球面深度投影得到球面全景图;为了提高识别精度,将每个模型的球面全景图从多个角度展开,创建多幅平面图像作为识别系统的输入;识别系统使用多分支的卷积神经网络,并将多幅全景图进行整合分析,最终得到一个三维模型的识别结果.对三维模型进行分类和检索的实验结果表明,文中方法的识别效果优于近年来的前沿方法,对三维模型进行检索的准确度甚至超过了多视图识别方法.%3D shape recognition is a hot topic in recent years. This paper proposed a 3D model recognition method with multi-branch convolutional neural network (CNN) to address the problems of 3D shape representa-tion and recognition. The inputs of the proposed method are spherical panoramas by deep spherical projection of 3D models; to improve recognition accuracy, the spherical panorama of the shape first unfolded on various orien-tations to produce multiple rectified images as input of recognition frame; the recognition system consists of a multi-branch CNN, which analyzes the panoramas as a whole to produce the final recognition result. The experi-ment results of retrieval and classification on various of 3D dataset showed that the performance of our method is better than the state-of-the-art methods, and the retrieval accuracy outperforms that of multi-view method.
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