SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds

  • Xiaolong MAO
  • , Hui YUAN
  • , Tian GUO
  • , Shiqi JIANG
  • , Raouf HAMZAOUI
  • , Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model. The frequency sampling module uses a Hamming window and the Fast Fourier Transform to extract high-frequency components of the point cloud. The difference between the original point cloud and the sampled point cloud is divided into multiple sub-point clouds. These sub-point clouds are then partitioned using an octree, providing a structured input for feature extraction. The feature extraction module integrates adaptive convolutional layers and uses offset-attention to capture both local and global features. Then, a geometry-assisted attribute feature refinement module is used to refine the extracted attribute features. Finally, a global hyperprior model is introduced for entropy encoding. This model propagates hyperprior parameters from the deepest (base) layer to the other layers, further enhancing the encoding efficiency. At the decoder, a mirrored network is used to progressively restore features and reconstruct the color attribute through transposed convolutional layers. The proposed method encodes base layer information at a low bitrate and progressively adds enhancement layer information to improve reconstruction accuracy. Compared to the best anchor of the latest geometry-based point cloud compression (G-PCC) standard that was proposed by the Moving Picture Experts Group (MPEG), the proposed method can achieve an average Bjøntegaard delta bitrate of -24.58% for the Y component (resp. -21.23% for YUV components) on the MPEG Category Solid dataset and -22.48% for the Y component (resp. -17.19% for YUV components) on the MPEG Category Dense dataset. This is the first instance that a learning-based attribute codec outperforms the G-PCC standard on these datasets by following the common test conditions specified by MPEG.
Original languageEnglish
Pages (from-to)2939-2953
Number of pages15
JournalIEEE Transactions on Image Processing
Volume34
Early online date12 May 2025
DOIs
Publication statusPublished - May 2025

Bibliographical note

Our source code will be made publicly available on https://github.com/sduxlmao/SPAC.

Publisher Copyright:
© 1992-2012 IEEE.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62222110 and Grant 62172259, in part by the High-End Foreign Experts Recruitment Plan of Chinese Ministry of Science and Technology under Grant G2023150003L, in part by Taishan Scholar Project of Shandong Province under Grant tsqn202103001, in part by Shandong Provincial Natural Science Foundation under Grant ZR2022ZD38, and in part by the OPPO Research Fund.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Point cloud compression
  • attribute compression
  • augmented reality
  • immersive communication
  • metaverse
  • point cloud sampling
  • scalable coding

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