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Hierarchical Prior-Based Super Resolution for Point Cloud Geometry Compression.
Summary
This study introduces a new method to improve Geometry-based Point Cloud Compression (G-PCC) by using a hierarchical prior for super-resolution. This technique significantly reduces distortions in lossy compression, enhancing point cloud quality.
Area of Science:
- Computer Vision
- Signal Processing
- Data Compression
Background:
- Geometry-based Point Cloud Compression (G-PCC) is essential for efficient data handling.
- Lossy G-PCC methods often introduce distortions due to simple geometry quantization like grid downsampling.
Purpose of the Study:
- To propose a novel hierarchical prior-based super-resolution method for enhancing point cloud geometry compression.
- To mitigate distortions in lossy G-PCC and improve reconstruction quality.
Main Methods:
- A content-dependent hierarchical prior is constructed at the encoder.
- This prior facilitates a coarse-to-fine super-resolution process at the decoder.
- The method's performance is evaluated using the MPEG Cat1A dataset.
Main Results:
- The proposed method achieves substantial Bjøntegaard-delta bitrate savings.
- Performance surpasses existing octree-based and trisoup-based G-PCC v14 methods.
- Experimental results confirm improved reconstruction accuracy.
Conclusions:
- Hierarchical prior-based super-resolution offers a significant advancement in point cloud geometry compression.
- The method effectively reduces distortions and improves compression efficiency over current standards.
- Open-source implementations are provided for reproducibility.
