  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.
