A Method for Enhancing the Quality of Decompressed Point Clouds Based on Attention-Fused Multi-Scale Features

ZHONG Xin; TANG Chun-ming; PENG Ling-xi

doi:10.12263/DZXB.20240914

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A Method for Enhancing the Quality of Decompressed Point Clouds Based on Attention-Fused Multi-Scale Features

PAPERS | 更新时间：2025-12-27

- A Method for Enhancing the Quality of Decompressed Point Clouds Based on Attention-Fused Multi-Scale Features
- ACTA ELECTRONICA SINICA Vol. 53, Issue 8, Pages: 2794-2804(2025)
- 作者机构：
  
  1.广州大学计算机科学与网络工程学院，广东广州510000
  2.广州大学数学与信息科学学院，广东广州510000
  3.广州大学机械与电气工程学院，广东广州510000
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20240914
  CLC： TP391
- Received：08 October 2024，
  
  Accepted：28 February 2025，
  
  Published：25 August 2025
- 稿件说明：
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钟芯, 唐春明, 彭凌西. 基于注意力融合多尺度特征的解压缩点云质量增强方法[J]. 电子学报, 2025, 53(08): 2794-2804.

ZHONG Xin, TANG Chun-ming, PENG Ling-xi. A Method for Enhancing the Quality of Decompressed Point Clouds Based on Attention-Fused Multi-Scale Features[J]. Acta Electronica Sinica, 2025, 53(08): 2794-2804.
钟芯, 唐春明, 彭凌西. 基于注意力融合多尺度特征的解压缩点云质量增强方法[J]. 电子学报, 2025, 53(08): 2794-2804. DOI：10.12263/DZXB.20240914

ZHONG Xin, TANG Chun-ming, PENG Ling-xi. A Method for Enhancing the Quality of Decompressed Point Clouds Based on Attention-Fused Multi-Scale Features[J]. Acta Electronica Sinica, 2025, 53(08): 2794-2804. DOI：10.12263/DZXB.20240914

摘要

基于几何的点云压缩算法（Geometry-based Point Cloud Compression，G-PCC）可以实现显著的点云压缩效率，但在低比特率场景下解压缩点云会产生严重的几何压缩伪影，并对整体视觉体验产生负面影响.为解决这一问题，本文提出了一种基于注意力融合多尺度特征的解压缩点云几何质量增强方法.具体地，该方法设计了多尺度输入模块对解压缩点云进行下采样操作，得到不同尺度的点云数据.接着，多尺度的点云被并行输入到离散卷积网络中提取从局部到全局的多尺度特征信息.最后，本文设计了跨尺度注意力特征融合模块来对多尺度特征进行融合，以增强特征的完整性和准确性.实验结果表明，本文所提出的方法在公开数据集上的平均峰值信噪比达到了67.968 4 dB，相较于标准压缩算法G-PCC提高了1.629 4 dB，主客观实验结果均表明本文方法能进一步提高解压缩点云的质量.

Abstract

Geometry-based point cloud compression (G-PCC) can achieve significant point cloud compression efficiency

but decompressing point clouds in low bit rate scenarios produces severe geometric compression artifacts and negatively affects the overall visual experience. To address this problem

this paper proposes a geometric quality enhancement method for decompressed point clouds based on attentional fusion of multiscale features. Specifically

the method designs a multi-scale input module to perform downsampling operations on the decompressed point cloud to obtain point cloud data at different scales. Then

the multi-scale point clouds are input in parallel into a discrete convolutional network to extract multi-scale feature information from local to global. Finally

a cross-scale attentional feature fusion module is designed in this paper to fuse the multi-scale features to enhance the completeness and accuracy of the features. The experimental results show that the proposed method achieves an average peak signal-to-noise ratio of 67.968 4 dB on the publicly available dataset

which is an improvement of 1.629 4 dB compared to the standard compression algorithm G-PCC

and the subjective and objective experimental results show that the method can further improve the quality of decompressed point clouds.

关键词

Keywords

references

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