3D Point Cloud Subspace Encoding to Reduce Decoder Processing Load
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Solution Overview
Problem
Existing three-dimensional data decoding devices face significant processing burdens due to the massive amount of data involved in point cloud representation, necessitating efficient compression and encoding methods.
Innovation Solution
A three-dimensional data encoding method that generates a bitstream by encoding subspaces within a space, incorporating a list of subspace information and identifiers in common control information, allowing for efficient decoding by referencing this information during the decoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is used to represent three-dimensional structures, then detailed shape information is achieved, but the data amount becomes massive requiring compression
Solution Approach 1:
The three-dimensional space is divided into multiple subspaces, each containing a subset of the point cloud data. This segmentation allows the massive data to be organized into manageable units that can be processed and transmitted more efficiently, reducing the processing burden on decoding devices while preserving the detailed shape information across all subspaces
Solution Approach 2:
The patent introduces a hierarchical dimension by organizing point cloud data into multiple levels of subspaces (e.g., root subspace, child subspaces). This dimensional organization transforms the flat massive dataset into a structured hierarchy, enabling efficient compression and selective decoding without losing the underlying three-dimensional shape precision
2Productivity
If compression encoding is applied to reduce data amount, then transmission efficiency is improved, but decoding processing complexity increases
Solution Approach 1:
The encoding process performs preliminary organization of point cloud data into a hierarchical subspace structure with assigned identifiers before compression. This preliminary structuring enables the decoder to efficiently navigate and process only the necessary subspaces without having to handle the entire dataset, thereby reducing decoding complexity while maintaining compression efficiency
Solution Approach 2:
The patent introduces control information as an intermediary layer that contains metadata about the subspace structure, identifiers, and relationships. This intermediary enables the decoder to efficiently interpret the compressed data without directly processing the raw point cloud structure, reducing computational complexity while preserving transmission efficiency
3Productivity
If subspace division with identifiers is implemented, then data organization efficiency is improved, but control information storage requirements increase
Solution Approach 1:
The control information structure is designed to serve multiple functions simultaneously: it stores subspace identifiers, defines hierarchical relationships, and provides indexing information. This multi-functionality reduces the need for separate data structures, thereby improving data organization efficiency while minimizing the overall control information storage requirements
Data Source
AI summary
A three-dimensional data encoding method includes: generating a bitstream by encoding subspaces included in a current space in which three-dimensional points are included. The bitstream includes encoded data respectively corresponding to the subspaces. In the generating of the bitstream, a list of information about the subspaces is stored in first control information included in the bitstream. The subspaces are respectively associated with identifiers assigned to the subspaces, and the first control information is common to the encoded data. Each of the identifiers assigned to the subspaces respectively corresponding to the encoded data is stored in a header of a corresponding one of the encoded data.