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How can we edit or transform the geometric or color property of a pointcloud? In this study, we propose a neural style transfer method for pointclouds which allows us to transfer the style of geometry or color from onepoint cloud either independently or simultaneously to another. This transfer isachieved by manipulating the content representations and Gram-based stylerepresentations extracted from a pre-trained PointNet-based classificationnetwork for colored point clouds. As Gram-based style representation isinvariant to the number or the order of points, the same method can be extendedto transfer the style extracted from an image to the color expression of apoint cloud by merely treating the image as a set of pixels. Experimentalresults demonstrate the capability of the proposed method for transferringstyle from either an image or a point cloud to another point cloud of a singleobject or even an indoor scene.

Neural Style Transfer for Point Clouds