Point Cloud Attribute Flag Encoding for Faster 3D Data Compression
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Solution Overview
Problem
Large sets of points in 3D space, such as those captured by LIDAR systems, generate significant data that is costly and time-consuming to store and transmit, limiting real-time applications and storage capacity.
Innovation Solution
A system that compresses attribute information of point clouds using a prediction-based transform, lifting scheme, or region-adaptive hierarchical transform, followed by variable-length encoding of significant and non-significant flag values, allowing for efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If point cloud attribute information is stored and transmitted without compression, then data accuracy and completeness are maintained, but storage costs and transmission time increase significantly
Solution Approach 1:
The patent extracts and separately encodes significance flags that indicate whether transformed attribute values are significant (non-zero) or non-significant (zero). This extraction allows the decoder to skip processing of non-significant values, reducing transmission time while maintaining accuracy of significant information.
Solution Approach 2:
The patent applies partial action by only fully encoding and transmitting significant attribute values (those with non-zero transformed coefficients), while non-significant values are represented by absence of encoding. This reduces overall transmission data volume and time while preserving essential information quality.
2Productivity
If point cloud attribute information is compressed using transforms, then data transmission efficiency improves, but computational complexity increases
Solution Approach 1:
The patent segments the attribute encoding process into distinct stages: organizing points by spatial position, applying transforms to generate transformed values, determining significance flags for each transformed value, and encoding only significant values. This segmentation allows each stage to be optimized independently, improving overall transmission efficiency while managing computational complexity through modular processing.
Solution Approach 2:
The patent applies different processing quality to different data elements based on their significance. Significant transformed attribute values receive full encoding and transmission, while non-significant values are represented more simply by omission. This local differentiation optimizes the balance between computational effort and transmission efficiency for each individual data element.
3Measurement precision
If all attribute values are encoded and transmitted, then reconstruction accuracy is maximized, but storage requirements increase
Solution Approach 1:
The patent extracts significance information from transformed attribute values and uses this extracted metadata to determine what data requires full encoding storage versus what can be represented by default zero values. This extraction enables selective storage that maintains reconstruction accuracy for significant values while minimizing storage for non-significant ones.
Solution Approach 2:
The patent changes the representation parameter for non-significant attribute values from full numerical encoding to a binary significance flag (or absence of encoding). This parameter change dramatically reduces storage requirements for non-significant values while the significance flags ensure that reconstruction accuracy is maintained for all significant values through selective full encoding.
Data Source
AI summary
A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information for the point cloud. To compress the attribute information, a transform is applied to the attribute values to generate attribute coefficients/transformed attribute values. Points with attribute coefficients with a significant value are assigned a first binary flag value, while points with non-significant attribute coefficients are assigned a second binary flag value. A Kth order exponential Golomb encoder or Golomb-Rice encoder is used to compress the run-length values, where separate states and associated contexts are maintained for funs of both the first and second binary values. A decoder uses a corresponding process to decode the compressed attribute information.