Progressive Point Cloud Upsampling via Differentiable Rendering
Pingping Zhang, Xu Wang, Lin Ma, Shiqi Wang, Sam Kwong, Jianmin Jiang
Abstract
In this paper, we propose one novel progressive point cloud upsampling framework to tackle the non-uniform distribution issue during the point cloud upsampling process. Specifically, we design an Up-UNet feature expansion module which is capable of learning the local and global point features via a down-feature operator and an up-feature operator, respectively, to alleviate the non-uniform distribution issue and remove the outliers. Moreover, we design a hybrid loss function considering both the multi-scale reconstruction loss and the rendering loss. The multi-scale reconstruction loss enables each upsampling module to generate a denser point cloud, while the rendering loss via point-based differentiable rendering ensures that the proposed model preserves the point cloud structures. Extensive experimental results demonstrate that our proposed model achieves state-of-the-art performance in terms of both qualitative and quantitative evaluations.