We present LBS-AE; a self-supervised autoencoding algorithm for fitting
articulated mesh models to point clouds. As input, we take a sequence of point
clouds to be registered as well as an artist-rigged mesh, i.e. a template mesh
equipped with a linear-blend skinning (LBS) deformation space parameterized by
a skeleton hierarchy. As output, we learn an LBS-based autoencoder that
produces registered meshes from the input point clouds. To bridge the gap
between the artist-defined geometry and the captured point clouds, our
autoencoder models pose-dependent deviations from the template geometry. During
training, instead of using explicit correspondences, such as key points or pose
supervision, our method leverages LBS deformations to bootstrap the learning
process. To avoid poor local minima from erroneous point-to-point
correspondences, we utilize a structured Chamfer distance based on
part-segmentations, which are learned concurrently using self-supervision. We
demonstrate qualitative results on real captured hands, and report quantitative
evaluations on the FAUST benchmark for body registration. Our method achieves
performance that is superior to other unsupervised approaches and comparable to
methods using supervised examples.

本研究使用自监督自编码算法通过利用 LBS 变形空间参数化骨骼分层结构来实现点云序列的自动化配准，并使用 Part - 分割的结构化 Chamfer 距离来解决点对点对应关系错误对模型配准的影响，实验结果表明，该方法在体注册等方面的绩效不输于监督学习方法。