Fractional Super-Resolution of Voxelized Point Clouds

Abstract

We present a method to super-resolve voxelized point clouds downsampled by a fractional factor, using lookup-tables (LUT) constructed from self-similarities from their own downsampled neighborhoods. The proposed method was developed to densify and to increase the precision of voxelized point clouds, and can be used, for example, as improve compression and rendering. We super-resolve the geometry, but we also interpolate texture by averaging colors from adjacent neighbors, for completeness. Our technique, as we understand, is the first specifically developed for intra-frame super-resolution of voxelized point clouds, for arbitrary resampling scale factors. We present extensive test results over different point clouds, showing the effectiveness of the proposed approach against baseline methods.