K-D Tree Point Cloud Encoding via Deviation Quantization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for encoding and decoding point clouds are inefficient in terms of storage, bandwidth, and processing power, as they do not effectively minimize code complexity and compression time while maintaining data accuracy.
Innovation Solution
The use of a k-D tree algorithm for recursive partitioning of geometric constructs, combined with quantization and entropy encoding, to reduce the representation of point clouds into b-bit integer values and efficiently store and communicate geometric data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional encoding methods are used for point clouds, then data accuracy is maintained, but storage space and bandwidth requirements increase
Solution Approach 1:
The point cloud data is segmented into multiple partitions using a k-D tree structure, dividing the geometric construct into hierarchical regions. This segmentation allows selective encoding of partition boundaries and point deviations rather than storing all raw coordinates, reducing storage requirements while maintaining reconstruction accuracy
Solution Approach 2:
The encoding method transforms the representation parameters from storing complete coordinate values to storing only deviation values from partition boundaries. This parameter change reduces the quantity of data needed to represent the point cloud while preserving the ability to reconstruct the original geometry with acceptable accuracy
2Loss of information
If traditional encoding methods are used for point clouds, then data completeness is preserved, but bandwidth requirements increase
Solution Approach 1:
The method extracts only the essential information needed for reconstruction by storing partition boundary definitions and point deviations rather than complete coordinate data. This extraction reduces the amount of data that must be transmitted over bandwidth-constrained channels while preserving the ability to reconstruct the point cloud
Solution Approach 2:
The representation parameters are changed from full coordinate storage to deviation-based encoding, where only the differences from known partition boundaries are stored and transmitted. This parameter transformation reduces bandwidth requirements while maintaining data completeness for reconstruction purposes
3Productivity
If complex encoding algorithms are used, then compression efficiency improves, but code complexity increases
Solution Approach 1:
The complex encoding problem is segmented into manageable steps: k-D tree construction, partition boundary identification, deviation calculation, and entropy encoding. This segmentation of the encoding process into discrete, well-defined stages improves compression efficiency while keeping each individual step relatively simple and well-structured
4Manufacturing precision
If detailed point cloud representation is used, then geometric precision is maintained, but processing power requirements increase
Solution Approach 1:
The k-D tree structure and partition boundaries are pre-computed and stored before the actual encoding of point deviations. This preliminary action organizes the geometric data in advance, allowing the encoding step to focus only on computing simple deviation values from known boundaries, thereby maintaining geometric precision while reducing processing power requirements during encoding
Data Source
Figure 1
Figure 2A~2F
Figure 2G~2K
AI summary
An encoder includes a processor, a buffer, and a memory. The memory includes code as instructions that cause the processor to perform a number of steps. The steps include quantizing geometric data associated with a geometric construct, partitioning the geometric construct, determining a number of points in the partition, generating a deviation value based on the number of points in the partition, storing the deviation value in the buffer, and entropy encoding the deviation value.