Tiered Hierarchical Coding for 6DoF Point Cloud Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for compressing point cloud data, particularly for 6DoF applications, face challenges in terms of resolution, bit-depth precision, and overall data handling, due to the reuse of hardware designed for 2D video encoding, leading to inefficiencies in processing power and format variability across different use cases.
Innovation Solution
A tier-based hierarchical coding method is employed to decompose point cloud data into multiple echelons or layers, each with varying coding formats and sampling rates, allowing for efficient encoding and decoding of 6DoF data, enabling progressive decoding and region-of-interest processing without requiring complete decoding of the entire signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing 2D video encoding hardware is reused for point cloud compression, then hardware availability is improved, but processing efficiency and resolution quality deteriorate
Solution Approach 1:
The point cloud data is segmented into multiple layers with different resolutions and bit-depths. Each layer can be processed independently, allowing efficient utilization of available hardware resources while maintaining overall processing efficiency through hierarchical decomposition of the encoding task.
Solution Approach 2:
The patent transitions from 2D video encoding to 3D point cloud encoding by adding a depth dimension. This is achieved through hierarchical coding that processes data at multiple resolution levels, enabling efficient 3D compression while adapting existing 2D encoding hardware capabilities.
2Manufacturing precision
If hierarchical tiered coding is used for point cloud compression, then compression efficiency and resolution quality are improved, but processing complexity increases
Solution Approach 1:
The encoding process is segmented into multiple tiers, each handling a specific resolution level. This segmentation allows complex 3D point cloud data to be processed in manageable stages, improving resolution quality while distributing processing complexity across hierarchical levels rather than concentrating it in a single complex stage.
Solution Approach 2:
The hierarchical coding maintains continuous useful action by ensuring that each tier builds upon the previous tier's output. Lower-resolution tiers provide a foundation that is progressively refined by higher-resolution tiers, maintaining processing efficiency while achieving high final resolution quality through cumulative refinement.
3Measurement precision
If complete decoding of the entire point cloud signal is performed, then decoding accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and processes only the necessary tiers for the desired output quality. Users can select specific tiers to decode based on their needs, extracting only the required level of detail from the hierarchical structure. This reduces processing time and computational resources while maintaining sufficient decoding accuracy for the intended application.
Solution Approach 2:
The hierarchical structure enables partial decoding where only the necessary tiers are processed. For applications requiring moderate quality, only lower tiers need to be decoded, providing sufficient accuracy without the excessive computational effort of complete high-fidelity decoding. This allows flexible trade-off between accuracy and processing time.
Data Source
AI summary
A method of encoding a three-dimensional point cloud. The method comprising: obtaining a set of points within the three-dimensional point cloud, a point within the set of points having a co-ordinate in three-dimensions; converting the points into a two-dimensional representation, wherein, for a point within the set of points, information describing the co-ordinate is represented as a location within the two-dimensional representation and a value at the location; and encoding the two-dimensional representation using a tier-based hierarchical coding format to output encoded data, wherein the tier-based hierarchical coding format encodes the two-dimensional representation as a plurality of layers, the plurality of layers representing echelons of data used to progressively reconstruct the signal at different levels of quality.