  How can we edit or transform the geometric or color property of a point
cloud? In this study, we propose a neural style transfer method for point
clouds which allows us to transfer the style of geometry or color from one
point cloud either independently or simultaneously to another. This transfer 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 same method can be extended
to transfer the style extracted from an image to the color expression of a
point cloud by merely treating the image as a set of pixels. Experimental
results demonstrate the capability of the proposed method for transferring
style from either an image or a point cloud to another point cloud of a single
object or even an indoor scene.
