V-PCC Bitstream Structure for Point Cloud Compression
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Solution Overview
Problem
Existing communication networks face challenges in efficiently decoding and transmitting large volumes of data associated with three-dimensional (3D) point clouds, which are crucial for applications like tele-presence, virtual reality, and dynamic 3D maps.
Innovation Solution
The implementation of a video-based point cloud compression (V-PCC) bitstream structure based on the ISO Base Media File Format (ISOBMFF), which allows for flexible storage and extraction of point cloud components, enabling efficient decoding and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of points in a point cloud is increased to realistically reconstruct objects and scenes, then the representation accuracy is improved, but the data volume increases making efficient storage and transmission difficult
Solution Approach 1:
The patent segments the point cloud data into multiple octants based on spatial division, allowing selective compression and transmission of only the necessary portions. This segmentation enables the system to maintain representation accuracy for important regions while reducing data volume for less critical areas, directly addressing the contradiction between accuracy and data quantity.
Solution Approach 2:
The patent applies different compression techniques and quality levels to different regions of the point cloud. Important regions maintain higher quality representation while less important regions use lower quality compression, allowing the system to optimize the balance between overall representation accuracy and total data volume.
2Measurement precision
If the number of points is increased to millions or billions for realistic 3D reconstruction, then the detail and realism are improved, but the complexity of storage and transmission increases
Solution Approach 1:
By dividing the point cloud into octants and further into smaller regions, the patent reduces the complexity of handling massive datasets. Each segment can be processed, stored, and transmitted independently, making the overall system more manageable while maintaining the detail required for realistic reconstruction.
Solution Approach 2:
The patent implements a nested structure where point cloud data is organized in hierarchical levels (octants, regions, clusters), allowing efficient indexing and selective access. This nested organization reduces transmission and storage complexity by enabling the system to load and process only the necessary nested levels required for a given application.
3Quantity of substance
If existing compression methods are used for point clouds, then some data reduction is achieved, but the decoding efficiency and transmission performance remain insufficient for large-scale applications
Solution Approach 1:
The patent implements dynamic adaptation in the compression system, allowing the decoder to adjust its processing based on the actual data characteristics and application requirements. This dynamic approach optimizes decoding efficiency by adapting to varying point cloud sizes, densities, and importance levels, directly improving productivity for large-scale applications.
Solution Approach 2:
The system incorporates feedback mechanisms that allow the encoder and decoder to continuously optimize their performance based on processing results. This feedback loop enables real-time adjustment of compression parameters to maximize both data reduction and decoding efficiency, addressing the insufficiency of existing static compression methods.
Data Source
AI summary
Methods, apparatus, systems, architectures and interfaces for encoding and/or decoding point cloud bitstreams including coded point cloud sequences are provided. Included among such methods, apparatuses, systems, architectures, and interfaces is an apparatus that may include a processor and memory. A method may include any of: mapping components of the point cloud bitstream into tracks; generating information identifying any of geometry streams or texture streams according to the mapping of the components; generating information associated with layers corresponding to respective geometry component streams; and generating information indicating operation points associated with the point cloud bitstream.