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.

本研究旨在对非刚性三维人体模型的形状检索算法进行基准测试，并通过添加新的训练集数据，对先前的检索算法基准测试结果进行更新和扩充。共使用了 25 种不同的形状检索方法进行实验，并在 FAUST 人体扫描数据集上进行测试。