With the rapid development and wide application of computer, camera device, network and hardware technology, 3D object (or model) retrieval has attracted widespread attention and it has become a hot research topic in the computer vision domain. Deep learning features already available in 3D object retrieval have been proven to be better than the retrieval performance of hand-crafted features. However, most existing networks do not take into account the impact of multi-view image selection on network training, and the use of contrastive loss alone only forcing the same-class samples to be as close as possible. In this work, a novel solution named Multi-view Discrimination and Pairwise CNN (MDPCNN) for 3D object retrieval is proposed to tackle these issues. It can simultaneously input of multiple batches and multiple views by adding the Slice layer and the Concat layer. Furthermore, a highly discriminative network is obtained by training samples that are not easy to be classified by clustering. Lastly, we deploy the contrastive-center loss and contrastive loss as the optimization objective that has better intra-class compactness and inter-class separability. Large-scale experiments show that the proposed MDPCNN can achieve a significant performance over the state-of-the-art algorithms in 3D object retrieval.

随着计算机，相机设备，网络和硬件技术的飞速发展和广泛应用，3D对象（或模型）的检索已引起广泛关注，并已成为计算机视觉领域的研究热点。3D对象检索中已经提供的深度学习功能已被证明比手工制作的功能具有更好的检索性能。但是，大多数现有网络都没有考虑多视图图像选择对网络训练的影响，仅使用对比损失就只能迫使相同类别的样本尽可能接近。在这项工作中，一种名为多视图歧视和成对CNN（MDPCNN）提出了用于3D对象检索的解决方案。通过添加Slice层和Concat层，它可以同时输入多个批次和多个视图。此外，通过训练不易于通过聚类进行分类的样本，可以获得高区分性的网络。最后，我们将对比中心损失和对比损失作为具有更好的类内紧凑性和类间可分离性的优化目标。大规模实验表明，与3D对象检索中的最新算法相比，提出的MDPCNN可以实现显着的性能。