PSNet: A Style Transfer Network for Point Cloud Stylization on Geometry and Color
Xu Cao, Weimin Wang, Katashi Nagao, Ryosuke Nakamura
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 pretrained 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.