Octree Point Cloud Compression with Adaptive Lookup Tables
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Solution Overview
Problem
Point clouds captured by sensors like LIDAR systems and 3-D cameras are large and costly to store and transmit, limiting their use in real-time applications due to significant storage and network resource requirements.
Innovation Solution
A system that compresses point cloud data using an octree-based encoding technique, partitioning points into cubes and sub-cubes, and using look-up tables and caches to efficiently encode and decode occupancy symbols, reducing the number of bits required for representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If point cloud data is stored and transmitted in original format, then data完整性 is maintained, but storage space and network bandwidth requirements increase significantly
Solution Approach 1:
The patent divides the point cloud space into an octree structure with multiple levels of cubes. Each cube is further divided into eight sub-cubes, creating a hierarchical segmentation that allows selective encoding of only occupied regions. This segmentation enables efficient compression by processing small, manageable portions of the point cloud independently.
Solution Approach 2:
The patent extracts and encodes only the occupancy information (occupied/unoccupied status) of sub-cubes rather than transmitting all point cloud data. By taking out only the essential occupancy symbols and encoding them using arithmetic coding with adaptive look-up tables, the system reduces data quantity while preserving the structural information needed for reconstruction.
2Quantity of substance
If point cloud data is compressed using traditional methods, then storage space is reduced, but compression speed and real-time processing capability deteriorate
Solution Approach 1:
The patent performs preliminary actions by pre-building adaptive look-up tables and caches during an initialization phase. These data structures are prepared in advance based on statistical properties of occupancy symbols, enabling fast encoding operations during actual point cloud processing without requiring complex calculations at encoding time.
Solution Approach 2:
The patent implements dynamic adaptation by updating look-up tables and caches based on the actual distribution of occupancy symbols encountered during encoding. The system dynamically adjusts encoding parameters and table structures to match the specific characteristics of each point cloud dataset, optimizing compression speed for real-time applications.
3Device complexity
If occupancy symbols are encoded using fixed-length binary representation, then encoding simplicity is maintained, but encoding efficiency and compression ratio decrease
Solution Approach 1:
The patent changes the encoding parameters from fixed-length binary representation to variable-length arithmetic coding. By adjusting the number of bits used for encoding based on the frequency and importance of different occupancy symbols, the system achieves higher compression efficiency while maintaining relatively simple implementation through the use of arithmetic coding algorithms.
Solution Approach 2:
The patent introduces adaptive look-up tables and arithmetic coding as intermediary mechanisms between the occupancy symbol generation and final bit stream output. These intermediaries translate the simplified occupancy symbols into highly compressed binary representations by leveraging statistical patterns and adaptive encoding strategies.
4Productivity
If look-up tables and caches are implemented for occupancy symbol encoding, then encoding efficiency is improved, but device complexity and memory requirements increase
Solution Approach 1:
The patent implements a nested structure where look-up tables and caches are organized within the octree hierarchy. Smaller lookup structures are associated with leaf nodes while larger structures are maintained at higher levels. This nesting allows the system to manage complexity by only maintaining detailed lookup tables where necessary, reducing overall memory requirements.
Solution Approach 2:
The patent applies partial action by implementing look-up tables and caches only for the most frequently encountered occupancy symbols and at the most relevant octree levels. Rather than maintaining comprehensive structures for all possible cases, the system focuses resources on the partial set of data that provides the majority of compression benefits.
Data Source
AI summary
An encoder is configured to compress point cloud geometry information using an octree geometric compression technique that utilizes a binary arithmetic encoder, a look-ahead table, a cache, and a context selection process, wherein encoding contexts are selected based, at least in part, on neighborhood configurations. In a similar manner, a decoder is configured to decode compressed point cloud geometry information utilizing a binary arithmetic encoder, a look-ahead table, a cache, and a context selection process.