In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion. Firstly, we present the channel-attentive EdgeConv to fully exploit the local structures as well as the global shape in point features. Secondly, we observe that the concatenation manner used by vanilla foldings limits its potential of generating a complex and faithful shape. Enlightened by the success of StyleGAN, we regard the shape feature as style code that modulates the normalization layers during the folding, which considerably enhances its capability. Thirdly, we realize that existing point supervisions, e.g., Chamfer Distance or Earth Mover's Distance, cannot faithfully reflect the perceptual quality of the reconstructed points. To address this, we propose to project the completed points to depth maps with a differentiable renderer and apply adversarial training to advocate the perceptual realism under different viewpoints. Comprehensive experiments on ShapeNet and KITTI prove the effectiveness of our method, which achieves state-of-the-art quantitative performance while offering superior visual quality.

翻译：在本文中,我们提出了一个新型的基于样式的点子生成器(SpareNet),用于完成点云。首先,我们展示了频道高级边缘Conv,以充分利用地方结构以及全球的点形特征。第二,我们观察到香草折叠所使用的汇合方式限制了其产生复杂和忠实形状的潜力。在StelegGAN的成功启发下,我们把形状特征视为在折叠过程中调节正常化层的风格代码,这大大增强了它的能力。第三,我们认识到现有的点监督器,例如Champer距离或地球移动器距离,无法忠实地反映重塑点的感知质量。为了解决这一问题,我们提议用不同的投影器来预测完成的点,并应用对抗性培训来在不同观点下倡导概念现实主义。关于ShapeNet和KITTI的全面实验证明了我们方法的有效性,在提供高水平视觉质量的同时,实现了最先进的定量表现。