PSNet: A Style Transfer Network for Point Cloud Stylization on Geometry and Color
Abstract
We propose a neural style transfer method for colored point clouds which allows stylizing the geometry and/or color property of a point cloud from another. The stylization is achieved by manipulating the content representations and Gram-based style representations extracted from a pre-trained PointNet-based classification network for colored point clouds. As Gram-based style representation is invariant to the number or the order of points, the style can also be an image in the case of stylizing the color property of a point cloud by merely treating the image as a set of pixels. Experimental results and analysis demonstrate the capability of the proposed method for stylizing a point cloud either from another point cloud or an image.
Cite
Text
Cao et al. "PSNet: A Style Transfer Network for Point Cloud Stylization on Geometry and Color." Winter Conference on Applications of Computer Vision, 2020.Markdown
[Cao et al. "PSNet: A Style Transfer Network for Point Cloud Stylization on Geometry and Color." Winter Conference on Applications of Computer Vision, 2020.](https://mlanthology.org/wacv/2020/cao2020wacv-psnet/)BibTeX
@inproceedings{cao2020wacv-psnet,
title = {{PSNet: A Style Transfer Network for Point Cloud Stylization on Geometry and Color}},
author = {Cao, Xu and Wang, Weimin and Nagao, Katashi and Nakamura, Ryosuke},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2020},
url = {https://mlanthology.org/wacv/2020/cao2020wacv-psnet/}
}