Dynamic Point Cloud Compression via Inter-Prediction
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Solution Overview
Problem
Point clouds generated by sensors like LIDAR systems and 3D 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 process for compressing point cloud attribute data using both intra-prediction and inter-prediction methods, which involves predicting attribute values based on neighboring points, determining residual differences, and encoding these differences, along with motion compensation functions, to segment and compress the data efficiently across multiple moments in time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is stored and transmitted without compression, then data integrity and quality are maintained, but storage and network resource requirements become prohibitively large
Solution Approach 1:
The point cloud data is divided into multiple segments or blocks, allowing independent processing and compression of each segment. This segmentation enables efficient application of prediction techniques and facilitates parallel processing, reducing overall computational complexity while achieving effective compression ratios
Solution Approach 2:
The patent uses prediction techniques where attribute values of points are estimated by copying or interpolating from neighboring points or previously processed frames. This creates predicted versions of the original data that can be stored in compressed form, with only the differences (residuals) needing to be transmitted, significantly reducing data volume while maintaining quality
2Loss of energy
If compression is applied to reduce storage and transmission costs, then resource efficiency improves, but processing complexity and computational requirements increase
Solution Approach 1:
The patent applies prediction techniques in advance during the encoding process, pre-computing predicted attribute values and residuals. This preliminary action prepares the data in an optimized format that reduces subsequent processing complexity during decoding and transmission, as the decompression process simply requires applying the inverse operations
Solution Approach 2:
The compression approach dynamically adapts to the characteristics of the point cloud data by adjusting prediction parameters and processing strategies based on data patterns. This dynamic adaptation allows the system to optimize compression ratios while managing computational complexity according to the specific properties of each data set
3Productivity
If real-time processing is implemented, then application responsiveness improves, but storage and network bandwidth requirements become more critical
Solution Approach 1:
By using prediction-based compression where most attribute data is derived through copying or interpolation from reference data, the patent dramatically reduces the actual data volume that needs to be transmitted in real-time. Only the residual differences and necessary prediction parameters need transmission, enabling real-time applications with limited bandwidth
Solution Approach 2:
The segmentation of point cloud data enables selective transmission and processing of critical segments in real-time applications. Important regions can be prioritized for transmission while less critical areas are compressed more aggressively or transmitted with lower fidelity, optimizing real-time performance under bandwidth constraints
Data Source
AI summary
A system comprises an encoder configured to compress attribute information for a dynamic point cloud and/or a decoder configured to decompress compressed attribute information for a dynamic point cloud. The dynamic point cloud may include multiple versions of the point cloud at multiple moments in time Attribute values for the point cloud may be compressed at a reference frame using an intra-prediction process and may be compressed at one or more reference frames using an inter-prediction process that takes advantage of temporal relationships between different frames (e.g. versions) of the dynamic point cloud at the different moments in time.