论文标题
频率选择性几何形状提升点云
Frequency-Selective Geometry Upsampling of Point Clouds
论文作者
论文摘要
在过去的几年中，对高分辨率点云的需求有所增加。但是，捕获高分辨率点云很昂贵，因此经常被低分辨率数据的上采样所取代。大多数最先进的方法要么仅限于栅格网格，要么包含正常向量，要么接受单个用例训练。我们建议使用频率选择性原理，其中局部估算了频率模型，该模型近似于点云的表面。然后，将其他点插入近似表面。与最新的缩放因子2和4相比，我们的新型频率选择性几何形状上采样在主观和客观质量方面表现出较高的结果。平均而言，我们提出的方法比第二最佳的最佳状态PU-NET较小的4.4倍误差4.4倍。
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 low-resolution 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-of-the-art PU-Net for a scale factor of 4.
