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。

非刚性三维人体模型的形状检索

Shape retrieval of non-rigid 3d human models

This paper reports on a novel template-free monocular non-rigid surface
reconstruction approach. Existing techniques using motion and deformation cues
rely on multiple prior assumptions, are often computationally expensive and do
not perform equally well across the variety of data sets. In contrast, the
proposed Scalable Monocular Surface Reconstruction (SMSR) combines strengths of
several algorithms, i.e., it is scalable with the number of points, can handle
sparse and dense settings as well as different types of motions and
deformations. We estimate camera pose by singular value thresholding and
proximal gradient. Our formulation adopts alternating direction method of
multipliers which converges in linear time for large point track matrices. In
the proposed SMSR, trajectory space constraints are integrated by smoothing of
the measurement matrix. In the extensive experiments, SMSR is demonstrated to
consistently achieve state-of-the-art accuracy on a wide variety of data sets.

本文报道了一种新颖的无模板单目非刚性表面重建方法，称为可扩展单目表面重建（SMSR），它结合了多种算法的优势，可以处理不同类型的运动和变形，并在各种数据集上始终达到最先进的准确性。