PSNet: A Style Transfer Network for Point Cloud Stylization on Geometry and Color

Xu Cao, Weimin Wang, Katashi Nagao, Ryosuke Nakamura; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 3337-3345

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.

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[bibtex]
@InProceedings{Cao_2020_WACV,
author = {Cao, Xu and Wang, Weimin and Nagao, Katashi and Nakamura, Ryosuke},
title = {PSNet: A Style Transfer Network for Point Cloud Stylization on Geometry and Color},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}