K-D Tree Point Cloud Encoding Using Deviation Values
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Solution Overview
Problem
Existing methods for encoding and decoding point clouds within geometric constructs are inefficient in terms of space usage, processing speed, and code complexity, particularly when dealing with large datasets and high-dimensional shapes.
Innovation Solution
The proposed solution involves using a k-D tree-based approach to partition geometric constructs, quantizing geometric data, determining deviation values based on point counts in partitions, and entropy encoding these values to efficiently store and communicate point cloud data, utilizing a processor, buffer, and memory with specific instructions for encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is stored and communicated using existing encoding methods, then the data can be represented, but the space usage (bandwidth and memory) is excessive
Solution Approach 1:
The geometric construct is recursively partitioned into multiple partitions using a k-D tree structure, dividing the point cloud data into hierarchical segments. This segmentation allows efficient encoding by processing and storing only the necessary deviation values and point counts for each partition level, significantly reducing storage space while maintaining data integrity
Solution Approach 2:
The patent transforms the representation parameters by encoding deviation values (difference between actual and expected point counts) instead of storing complete point cloud coordinates. This parameter transformation reduces the quantity of data needed to represent the point cloud while preserving the ability to accurately reconstruct the original data
2Productivity
If existing encoding methods are used for point clouds, then data can be stored, but processing speed is slow
Solution Approach 1:
The k-D tree structure is pre-built during the encoding phase, organizing point cloud data into a hierarchical partitioning structure before compression. This preliminary organization enables faster decoding and processing by avoiding repeated computational geometry operations, thereby improving processing speed while managing complexity through structured preprocessing
Solution Approach 2:
The point cloud is divided into hierarchical partitions through k-D tree segmentation, allowing parallel processing and efficient memory access patterns. This segmentation improves processing speed by enabling batch operations on partitioned data and reducing the computational complexity of individual processing steps
3Quantity of substance
If point cloud data is compressed to reduce bandwidth, then communication efficiency improves, but code complexity increases
Solution Approach 1:
The patent changes the encoding parameters from storing complete point cloud coordinates to storing deviation values and point counts relative to the k-D tree structure. This parameter transformation reduces bandwidth requirements by encoding only the differences from expected values, while the systematic approach to calculating and encoding deviations keeps the complexity manageable
Data Source
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.