3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods.

中文翻译：

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非刚性 3d 人体模型的形状检索

人类的 3D 模型通常用于计算机图形和视觉，因此区分身体形状的能力是一个重要的形状检索问题。我们扩展了我们最近的论文，该论文为在 3D 人体模型上测试非刚性 3D 形状检索算法提供了基准。这个基准提供了比以前的形状基准更严格的挑战。我们添加了 145 个新模型作为单独的训练集，以标准化所使用的训练数据并提供更公平的比较。我们还包括对人体扫描的 FAUST 数据集进行的实验。先前基准研究的所有参与者都参加了此处报告的新测试，其中许多人使用新数据提供了更新结果。此外，更多的参与者也参与了进来，我们提供了对检索结果的额外分析。