论文标题
非刚性3D人类模型的形状检索
Shape retrieval of non-rigid 3d human models
论文作者
论文摘要
人类的3D模型通常在计算机图形和视觉中使用，因此区分身体形状的能力是一个重要的形状检索问题。我们扩展了最近的论文，该论文为在3D人类模型上测试非刚性3D形状检索算法提供了基准。该基准比以前的形状基准提出了更严格的挑战。我们添加了145个新模型作为单独的培训集，以便标准化所使用的培训数据并提供更公平的比较。我们还包括了人类扫描的浮士德数据集的实验。先前基准研究的所有参与者都参加了此处报告的新测试，许多参与者使用新数据提供了更新的结果。此外，进一步的参与者也参加了比赛，我们还提供了额外的检索结果分析。总共25种不同的形状检索方法。
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.
