This paper investigates the problem of temporally interpolating dynamic 3D
point clouds with large non-rigid deformation. We formulate the problem as
estimation of point-wise trajectories (i.e., smooth curves) and further reason
that temporal irregularity and under-sampling are two major challenges. To
tackle the challenges, we propose IDEA-Net, an end-to-end deep learning
framework, which disentangles the problem under the assistance of the
explicitly learned temporal consistency. Specifically, we propose a temporal
consistency learning module to align two consecutive point cloud frames
point-wisely, based on which we can employ linear interpolation to obtain
coarse trajectories/in-between frames. To compensate the high-order nonlinear
components of trajectories, we apply aligned feature embeddings that encode
local geometry properties to regress point-wise increments, which are combined
with the coarse estimations. We demonstrate the effectiveness of our method on
various point cloud sequences and observe large improvement over
state-of-the-art methods both quantitatively and visually. Our framework can
bring benefits to 3D motion data acquisition. The source code is publicly
available at this https URL.

本文探究如何以点云数据为基础，应用深度学习方法以及掌握时间一致性的学习模块，实现对包含大量非刚性变形的 3D 动态场景进行时间插值的问题，并在各项实验中取得了比同类方法更好的效果。