Octree Point Cloud Compression with Adaptive Binary Arithmetic Encoding
Find Innovative SolutionsGenerate Solutions
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 spatial and attribute information of point clouds using an octree-based encoding technique, where occupancy symbols are encoded using a look-up table, cache, and adaptive binary arithmetic encoding to reduce 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 raw format, then data completeness and accuracy are maintained, but storage cost and transmission time increase significantly
Solution Approach 1:
The patent divides the point cloud space into an octree structure with multiple levels of cubes. Each cube can be independently processed and encoded, allowing selective transmission of only occupied cubes. This segmentation enables compression while preserving spatial relationships and point cloud completeness.
Solution Approach 2:
The patent extracts and encodes only the essential occupancy information (which cubes contain points) rather than transmitting all point data. By taking out only the necessary occupancy symbols and encoding them efficiently, the system reduces storage requirements while maintaining the ability to reconstruct the complete point cloud.
2Ease of manufacture
If occupancy symbols are encoded using fixed binary representation, then encoding simplicity is maintained, but compression efficiency decreases due to uniform bit allocation
Solution Approach 1:
The patent implements dynamic encoding where the number of bits required to represent an occupancy symbol varies based on its frequency and position in the octree. Frequently occurring symbols use fewer bits, while rare symbols use more bits, creating an adaptive encoding scheme that optimizes compression efficiency without sacrificing too much encoding complexity.
Solution Approach 2:
The patent changes the encoding parameters (number of bits, bit distribution) based on the specific characteristics of each occupancy symbol and its context in the octree structure. This allows the system to adjust the binary representation dynamically to match the actual data distribution patterns.
3Measurement precision
If look-up table size is increased to include all possible occupancy symbols, then encoding accuracy is improved, but memory consumption and processing time increase
Solution Approach 1:
The patent uses a partial look-up table that includes only the most frequently occurring occupancy symbols rather than all possible symbols. This partial approach provides sufficient encoding accuracy for the majority of cases while keeping the table size manageable. The system handles rare symbols through alternative encoding paths, achieving a balance between accuracy and complexity.
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.