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View-Based 3D Model Retrieval via Convolutional Neural Networks

机译:卷积神经网络的基于视图的3D模型检索

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In this paper, we propose a multi-view fusion 3D model retrieval using convolutional neural network to solve the problem of the local perception in feature descriptor. By view pooling, we combine information from multiple views of a 3D model to eliminate the position correlation caused by the viewing angle of camera. In addition, integrating pre-processed RGB view-feature with Binary view-feature in the same model is used to generate a single model descriptor. Experiments on ETH dataset demonstrate the superiority of the proposed method.
机译:在本文中,我们提出了一种使用卷积神经网络的多视图融合3D模型检索,以解决特征描述符中的局部感知问题。通过视图池,我们将来自3D模型多个视图的信息组合在一起,以消除由摄像机视角引起的位置相关性。另外,在同一模型中集成预处理的RGB视图特征和Binary视图特征可用于生成单个模型描述符。在ETH数据集上的实验证明了该方法的优越性。

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