  Object reconstruction from 3D point clouds has been a long-standing research
problem in computer vision and computer graphics, and achieved impressive
progress. However, reconstruction from time-varying point clouds (a.k.a. 4D
point clouds) is generally overlooked. In this paper, we propose a new network
architecture, namely RFNet-4D++, that jointly reconstructs objects and their
motion flows from 4D point clouds. The key insight is simultaneously performing
both tasks via learning of spatial and temporal features from a sequence of
point clouds can leverage individual tasks, leading to improved overall
performance. To prove this ability, we design a temporal vector field learning
module using an unsupervised learning approach for flow estimation task,
leveraged by supervised learning of spatial structures for object
reconstruction. Extensive experiments and analyses on benchmark datasets
validated the effectiveness and efficiency of our method. As shown in
experimental results, our method achieves state-of-the-art performance on both
flow estimation and object reconstruction while performing much faster than
existing methods in both training and inference. Our code and data are
available at https://github.com/hkust-vgd/RFNet-4D
