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.

中文翻译：

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PF-Net：用于 3D 点云完成的点分形网络

在本文中，我们提出了一种点分形网络（PF-Net），这是一种用于精确和高保真点云完成的新型基于学习的方法。与现有点云完成网络从不完整点云生成点云的整体形状并始终改变现有点并遇到噪声和几何损失不同，PF-Net保留了不完整点云的空间排列并可以计算出预测中缺失区域的详细几何结构。为了成功完成这项任务，PF-Net 通过利用基于特征点的多尺度生成网络来分层估计丢失的点云。此外，我们将多阶段完成损失和对抗性损失相加以生成更逼真的缺失区域。对抗性损失可以更好地处理预测中的多种模式。我们的实验证明了我们的方法对几个具有挑战性的点云完成任务的有效性。