3D Point Cloud Encoding with Adaptive Bit Count Metadata
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Solution Overview
Problem
Current three-dimensional data encoding methods lack efficiency in compressing and transmitting point cloud data, leading to high bandwidth requirements and inadequate support for multiplexing and decoding processes, especially when involving multiple codecs and formats.
Innovation Solution
A three-dimensional data encoding method that calculates a prediction residual between point cloud data and a predicted value, generating a bitstream with information on the residual, its bit count, and additional metadata to improve coding efficiency, and a corresponding decoding method to reconstruct the data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If point cloud data is compressed using conventional encoding methods, then data transmission bandwidth is reduced, but coding efficiency remains insufficient
Solution Approach 1:
The encoding process segments point cloud data into multiple layers including geometry information, attribute information, and occupancy maps. Each layer is encoded separately with optimized bit allocation, allowing efficient compression while maintaining the ability to reconstruct the full three-dimensional data at appropriate quality levels
Solution Approach 2:
The patent employs adaptive parameter changes including dynamic bit count allocation for prediction residuals, adjustable precision for three-dimensional point coordinates, and flexible quantization parameters for attribute information. These parameter adjustments optimize the balance between compression ratio and reconstruction quality based on data characteristics
2Measurement precision
If detailed three-dimensional point information is transmitted, then decoding accuracy is improved, but data volume increases
Solution Approach 1:
The encoding process performs preliminary prediction of three-dimensional point attributes using occupancy maps and reference point data before encoding the actual residual information. This preliminary action allows the decoder to reconstruct points with high accuracy using only the compact residual data, significantly reducing the volume of information that must be transmitted
Solution Approach 2:
The patent introduces intermediate data structures including occupancy maps, prediction residual data, and layer-coded attribute information that serve as mediators between the original point cloud and the final decoded output. These intermediaries enable efficient compression while preserving decoding accuracy through structured information representation
3Adaptability or versatility
If multiple codecs and formats are supported, then system versatility is improved, but processing complexity increases
Solution Approach 1:
The patent creates a universal bitstream structure that can represent multiple three-dimensional data formats and codecs within a single standardized framework. The encoded data includes flexible metadata and parameter sets that allow the same decoding architecture to handle different point cloud representations, attribute types, and geometry encoding methods, achieving multi-functionality without requiring separate processing paths for each format
Data Source
AI summary
A three-dimensional data encoding method includes: calculating a prediction residual that is a difference between information of a three-dimensional point included in point cloud data and a predicted value; and generates a bitstream including first information with respect to the prediction residual, second information indicating a bit count of the first information, and third information indicating a bit count of the second information.