Point Cloud Attribute Coding With Hash-Based Prediction
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Solution Overview
Problem
Existing point cloud coding methods are inefficient due to the high computational cost of finding nearest neighbors in 3D space and failure to utilize correlations among color channels, leading to performance losses in coding efficiency.
Innovation Solution
The implementation of hash-based prediction schemes and inter-component correlations using a hierarchical structure of hash tables to reconstruct and decode point cloud attribute data, allowing for efficient coding and decoding without requiring nearest neighbor searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If nearest neighbor search methods are used for point cloud coding, then prediction accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the point cloud data by organizing points into hierarchical structures (e.g., octrees, k-d trees) and grouping them by spatial regions. This segmentation allows the coding process to operate on smaller, localized subsets of data rather than searching the entire point cloud, thereby maintaining prediction accuracy within regions while reducing overall computational cost.
Solution Approach 2:
The patent performs preliminary organization of point cloud data into hierarchical spatial structures before the actual coding process. By pre-computing and storing spatial relationships, neighbor relationships, and attribute correlations in these hierarchical structures, the system eliminates the need for costly real-time nearest neighbor searches during coding, thus reducing computational cost while preserving prediction accuracy.
2Productivity
If traditional point cloud coding methods are used, then coding simplicity is maintained, but coding efficiency deteriorates due to failure to utilize color channel correlations
Solution Approach 1:
The patent merges the coding of multiple color channels (e.g., R, G, B) by exploiting their correlations. Instead of coding each channel independently, the method combines information across channels, using the correlation between them to improve compression efficiency. This merging approach reduces the total number of bits required to represent the point cloud while capturing inter-channel relationships.
Solution Approach 2:
The patent introduces intermediate representations that capture correlations among color channels. These intermediates serve as mediators that encode the relationships between channels, allowing the decoder to reconstruct the original color information more efficiently. The intermediates act as a bridge that utilizes channel correlations without requiring complex direct processing of all channel combinations.
3Measurement precision
If nearest neighbor searches are performed in 3D space, then attribute prediction accuracy is improved, but transmission time increases due to high computational cost
Solution Approach 1:
The patent segments the 3D space into hierarchical regions (e.g., using octrees or k-d trees) and performs attribute prediction within these segmented regions. By limiting the search space to local regions rather than the entire 3D space, the method maintains attribute prediction accuracy for nearby points while significantly reducing the time required to perform searches, thus decreasing transmission time.
Solution Approach 2:
The patent performs preliminary organization of points into hierarchical spatial structures and pre-computes neighbor relationships within these structures. This preliminary action eliminates the need for exhaustive 3D space searches during transmission, allowing for rapid attribute prediction and reducing overall transmission time while maintaining prediction accuracy.
Data Source
AI summary
A method, computer program, and computer system is provided for point cloud coding. Data corresponding to a point cloud is received. Hash elements corresponding to attribute values associated with the received data is reconstructed. A size of a hash table may be decreased based on deleting one or more of the hash elements corresponding to non-border regions associated with the attribute values. The data corresponding to the point cloud is decoded based on the reconstructed hash elements.