Frequency-Selective Geometry Upsampling of Point Clouds

Abstract

The demand for high-resolution point clouds has increased throughout the last years. However, capturing high-resolution point clouds is expensive and thus, frequently replaced by upsampling of lowresolution data. Most state-of-the-art methods are either restricted to a rastered grid, incorporate normal vectors, or are trained for a single use case. We propose to use the frequency selectivity principle, where a frequency model is estimated locally that approximates the surface of the point cloud. Then, additional points are inserted into the approximated surface. Our novel frequency-selective geometry upsampling shows superior results in terms of subjective as well as objective quality compared to state-of-the-art methods for scaling factors of 2 and 4. On average, our proposed method shows a 4.4 times smaller point-to-point error than the second best state-ofthe-art PU-Net for a scale factor of 4.

Graph Convolution Point Cloud Super-Resolution Network Based on Mixed Attention Mechanism

“…Later, Huang et al proposed a progressive method for edge resampling of points, but this method heavily relied on the accuracy of the initial normal direction and required strong prior knowledge to manually adjust the parameters [4]. Heimann V. et al proposed frequency-selective geometry upsampling for point cloud upsampling, which used DCT basis functions to locally estimate a continuous representation of the point cloud's surface [12]. In general, most of the traditional methods rely heavily on artificial prior knowledge, such as surface smoothing assumptions, normal direction estimation, etc., and lack the ability to obtain a priori independently from data.…”