View-based 3-D model retrieval is one of the most important techniques in numerous applications of computer vision. While many methods have been proposed in recent years, to the best of our knowledge, there is no benchmark to evaluate the state-of-the-art methods. To tackle this problem, we systematically investigate and evaluate the related methods by: 1) proposing a clique graph-based method and 2) reimplementing six representative methods. Moreover, we concurrently evaluate both hand-crafted visual features and deep features on four popular datasets (NTU60, NTU216, PSB, and ETH) and one challenging real-world multiview model dataset (MV-RED) prepared by our group with various evaluation criteria to understand how these algorithms perform. By quantitatively analyzing the performances, we discover the graph matching-based method with deep features, especially the clique graph matching algorithm with convolutional neural networks features, can usually outperform the others. We further discuss the future research directions in this field.

中文翻译：

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基于视图的 3D 模型检索：基准


基于视图的 3D 模型检索是计算机视觉众多应用中最重要的技术之一。尽管近年来提出了许多方法，但据我们所知，还没有评估最先进方法的基准。为了解决这个问题，我们通过以下方式系统地研究和评估相关方法：1）提出一种基于派系图的方法；2）重新实现六种代表性方法。此外，我们同时在四个流行的数据集（NTU60、NTU216、PSB 和 ETH）和我们小组使用各种评估标准准备的一个具有挑战性的现实世界多视图模型数据集（MV-RED）上评估手工制作的视觉特征和深度特征了解这些算法的执行方式。通过定量分析性能，我们发现具有深层特征的基于图匹配的方法，特别是具有卷积神经网络特征的派系图匹配算法，通常可以优于其他算法。我们进一步讨论了该领域未来的研究方向。