Wiener Filter-Based Point Cloud Adaptive Denoising for Video-based Point Cloud Compression

We propose a Wiener filter-based point cloud adaptive denoising method for video-based point cloud compression (V-PCC) platform. The proposed Wiener filter is conducted for two dimension (2D) geometry images generated by V-PCC platform. Due to the large local variation of pixel values in the 2D geometry image, a neighborhood differences-based adaptive filtering method is proposed. Specifically, pixels in a 2D geometry image are grouped according to their neighborhood differences and Wiener filter is performed to these categories seperately. In the decoder, Wiener filter will be applied to the distorted images by coefficients and other auxiliary information transmitted from the encoder. Experimental results show that an average -5.4% point-to-point geometry BD-Rate can be achieved by implementing our method on V-PCC, leading to a better subjective quality.