We introduce Neural Deformation Graphs for globally-consistent deformation
tracking and 3D reconstruction of non-rigid objects. Specifically, we
implicitly model a deformation graph via a deep neural network. This neural
deformation graph does not rely on any object-specific structure and, thus, can
be applied to general non-rigid deformation tracking. Our method globally
optimizes this neural graph on a given sequence of depth camera observations of
a non-rigidly moving object. Based on explicit viewpoint consistency as well as
inter-frame graph and surface consistency constraints, the underlying network
is trained in a self-supervised fashion. We additionally optimize for the
geometry of the object with an implicit deformable multi-MLP shape
representation. Our approach does not assume sequential input data, thus
enabling robust tracking of fast motions or even temporally disconnected
recordings. Our experiments demonstrate that our Neural Deformation Graphs
outperform state-of-the-art non-rigid reconstruction approaches both
qualitatively and quantitatively, with 64% improved reconstruction and 62%
improved deformation tracking performance.

本文提出一种使用深度神经网络建模变形图表达非刚性物体的全局一致变形跟踪和三维重建方法，能够在快速运动或时间不连续记录的情况下实现稳健的跟踪，并在明确的视点一致性和图形表面一致性约束下全局优化神经变形图进行自监督训练，同时利用隐式多层感知器形态表示对物体的形状进行优化，实验表明其效果优于现有的非刚性重建方法。