Gaussian point cloud lossless coding and decoding method for three-dimensional reconstruction

By optimizing the organization and encoding order of Gaussian point cloud data through Morton code sorting and AVS encoding/decoding technology, the problem of excessive Gaussian point cloud data volume is solved, and efficient compression and lossless encoding/decoding are achieved in mobile terminals and edge computing environments, which is suitable for 3D reconstruction of complex dynamic scenes.

CN121126005APending Publication Date: 2025-12-12GUANGDONG UNIV OF TECH
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Patent Information

Application Number
CN202511155189.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-18
Publication Date
2025-12-12

AI Technical Summary

Technical Problem

Existing technologies for 3D reconstruction involve excessive amounts of Gaussian point cloud data, resulting in heavy transmission and storage burdens and making it difficult to deploy effectively in mobile terminals and edge computing environments. Furthermore, traditional coding standards are not suitable for compressing unstructured Gaussian point cloud data, which hinders the development of immersive interactive applications.

Method used

A spatial sorting method based on Morton codes and AVS encoding/decoding technology are used to perform format conversion and encoding processing on Gaussian point cloud data to ensure data consistency. The geometric data and attribute data are rearranged through the Morton order index table to optimize the organization structure and encoding order of the point cloud data.

Benefits of technology

It significantly reduces the encoding bitrate while maintaining the same rendering quality, improves compression performance and data processing reliability, and is suitable for lossless encoding and decoding in complex dynamic scenes.

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Abstract

The invention relates to a three-dimensional reconstruction-oriented Gaussian point cloud lossless coding and decoding method, a product and a storage medium. The method comprises the following steps: extracting a first Gaussian point cloud corresponding to anhor data in a numpy array format at a coding side; converting the first Gaussian point cloud in the numpy array format into a first Gaussian point cloud in a ply format; meanwhile, through a Morton code-based spatial sorting method, performing Morton code sorting on geometric data and attribute data of the first Gaussian point cloud in the numpy array format, and generating a Morton sequence index table; and carrying out AVS coding on the first Gaussian point cloud in the ply format to obtain compressed data in a binary code stream form. On the decoding side, AVS decoding is carried out when the compressed data in the binary code stream form and the Morton sequence index table are received, and a second Gaussian point cloud in the ply format is obtained through reduction; performing format conversion on the second Gaussian point cloud in the ply format to obtain a second Gaussian point cloud in a numpy array format; and rearranging the attribute data and the geometric data based on the Morton sequence index table to realize one-to-one correspondence of the geometric data and the attribute data between the first Gaussian point cloud and the second Gaussian point cloud before and after coding and decoding. Compared with an existing method, the data consistency before and after coding is improved.
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