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首页> 外文期刊>Journal of visual communication & image representation >3D object recognition based on pairwise Multi-view Convolutional Neural Networks
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3D object recognition based on pairwise Multi-view Convolutional Neural Networks

机译：基于成对的多视图卷积神经网络的3D目标识别

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摘要

With the development of 3D sensors, it will be much easier for us to obtain 3D models, which is prevailing in our future daily life, but up to now, although many 3D object recognition algorithms have been proposed, there are some limitations, including the lack of training samples, hand-crafted feature representation, feature extraction and recognition separately. In this work, we propose a novel pairwise Multi-View Convolutional Neural Network for 3D Object Recognition (PMV-CNN for short), where automatic feature extraction and object recognition are put into a unify CNN architecture. Moreover, since the pairwise network architecture is utilized in PMV-CNN, thus, the requirement of the number of training samples in the original dataset is not severe. In addition, the latent complementary relationships from different views can be highly explored by view pooling. Large scale experiments demonstrate that the pairwise architecture is very useful when the number of labeled training samples is very small. Moreover, it also makes more robust feature extraction. Furthermore, since the end-to-end network architecture is employed in PMV-CNN, thus, the extracted feature is very suitable for 3D object recognition, whose performance is much better than that of hand-crafted features. In a word, the performance of our proposed method outperforms state-of-the-art methods. (C) 2018 Elsevier Inc. All rights reserved.
机译：随着3D传感器的发展，对于我们来说，获得3D模型将变得更加容易，这在我们未来的日常生活中很普遍，但是到目前为止，尽管已经提出了许多3D对象识别算法，但存在一些局限性，包括缺少训练样本，手工制作的特征表示，特征提取和识别分别存在。在这项工作中，我们提出了一种新颖的用于3D对象识别的成对多视图卷积神经网络（简称PMV-CNN），其中自动特征提取和对象识别被放入一个统一的CNN架构中。此外，由于在PMV-CNN中采用了成对网络架构，因此原始数据集中训练样本数量的要求并不严格。此外，可以通过视图池高度探索来自不同视图的潜在互补关系。大规模实验表明，当标记的训练样本数量很少时，成对架构非常有用。而且，它还使特征提取更加可靠。此外，由于在PMV-CNN中采用了端到端网络体系结构，因此提取的特征非常适合3D对象识别，其性能要比手工制作的特征好得多。简而言之，我们提出的方法的性能优于最新方法。 （C）2018 Elsevier Inc.保留所有权利。

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