In this paper, we propose a Point Fractal Network (PF-Net), a novel
learning-based approach for precise and high-fidelity point cloud completion.
Unlike existing point cloud completion networks, which generate the overall
shape of the point cloud from the incomplete point cloud and always change
existing points and encounter noise and geometrical loss, PF-Net preserves the
spatial arrangements of the incomplete point cloud and can figure out the
detailed geometrical structure of the missing region(s) in the prediction. To
succeed at this task, PF-Net estimates the missing point cloud hierarchically
by utilizing a feature-points-based multi-scale generating network. Further, we
add up multi-stage completion loss and adversarial loss to generate more
realistic missing region(s). The adversarial loss can better tackle multiple
modes in the prediction. Our experiments demonstrate the effectiveness of our
method for several challenging point cloud completion tasks.

本文提出了基于点分形网络的学习方法 Point Fractal Network（PF-Net），用于精确高保真度的点云补全，通过多尺度生成网络进行缺失点云的预测。同时，通过多阶段的补全误差和对抗损失来生成更真实的缺失区域，实验证明了该方法的有效性。