What is it about?
Recently, 3D point cloud has shown its potential in a wide range of fields. Due to its huge data volume, efficient compression for point cloud is taken into agenda. Aiming at reducing the distortion after Video-based Point Cloud Compression, we propose a neighborhood differences-based adaptive denoising method for distorted geometry images generated by V-PCC. Experimental results demonstrate the validity of the proposed method.
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Photo by Arseny Togulev on Unsplash
Why is it important?
We apply Wiener filter to the 2D geometry images so that the noise of 3D point cloud can be eliminated. Wiener filtering is a local algorithm, better results can be obtained if the neighborhood of each pixel is stable and similar. So pixels in a 2D geometry image are grouped according to their neighborhood differences and Wiener filter is performed to these categories separately. The optimal coefficients of the best category are transmitted to the decoder for image denoising.
Perspectives
Hopefully this article will get more people focused on point cloud compression, such as Video-based Point Cloud Compression (V-PCC) and Geometry-based Point Cloud Compression (G-PCC), as well as quality enhancement for distorted point clouds.
Jinrui Xing
Shandong University
Read the Original
This page is a summary of: Wiener Filter-Based Point Cloud Adaptive Denoising for Video-based Point Cloud Compression, October 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3552457.3555733.
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