Point Cloud Attribute Compression Using Hierarchical Prediction

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Solution Overview

Problem

Point clouds generated by sensors like LIDAR systems and 3D cameras contain large amounts of spatial and attribute information, making them costly and time-consuming to store and transmit, limiting their use in real-time applications due to significant storage and network resource requirements.

Innovation Solution

A system and method for compressing point cloud attribute information by assigning attribute values to points, determining predicted attribute values based on neighboring points, and encoding attribute correction values, which allows for efficient encoding and decoding of hierarchical levels of detail, using techniques such as octree encoding and binary arithmetic encoding.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If point cloud data is stored and transmitted in full detail, then high fidelity and complete attribute information are preserved, but storage costs and transmission time increase significantly

Engineering Contradiction:

Improveattribute information fidelityVSAvoidtransmission time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments point cloud data into multiple levels of detail (LOD), where coarse LOD contains sparse points with full attribute information and fine LOD contains dense points with compressed or predicted attribute information. This segmentation allows selective transmission of detail levels based on application requirements, reducing overall transmission time while preserving necessary fidelity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies partial action by transmitting only the necessary portion of attribute information at each LOD level. Instead of transmitting complete attribute data for all points, the system transmits full attributes only for coarse LOD points and uses prediction/interpolation for fine LOD points, achieving sufficient fidelity with reduced transmission time.

Inventive Principle:

Principle #16Partial or excessive action

2Measurement precision

If point cloud data is stored and transmitted in full detail, then high fidelity and complete attribute information are preserved, but storage costs increase significantly

Engineering Contradiction:

Improveattribute information fidelityVSAvoidstorage capacity

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent segments point cloud data into multiple levels of detail (LOD), where coarse LOD contains sparse points with full attribute information and fine LOD contains dense points with compressed or predicted attribute information. This segmentation allows selective storage of detail levels, reducing overall storage capacity requirements while preserving necessary fidelity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies partial action by storing only the necessary portion of attribute information at each LOD level. Instead of storing complete attribute data for all points, the system stores full attributes only for coarse LOD points and uses prediction/interpolation for fine LOD points, achieving sufficient fidelity with reduced storage capacity.

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If attribute information is compressed using prediction based on neighboring points, then storage and transmission efficiency improve, but encoding complexity increases

Engineering Contradiction:

Improvecompression efficiencyVSAvoidencoding complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the compression process into distinct stages: coarse LOD compression with full prediction and fine LOD compression with simplified prediction or residual encoding. This segmentation allows the system to achieve high compression efficiency while managing encoding complexity by applying different compression strategies to different data segments.

Inventive Principle:

Principle #1Segmentation

4Productivity

If hierarchical levels of detail are implemented, then storage and transmission efficiency improve, but decoding and reconstruction complexity increases

Engineering Contradiction:

Improvestorage efficiencyVSAvoiddecoding complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent applies preliminary action by pre-computing and storing prediction models, neighbor relationships, and interpolation parameters during the encoding phase. This allows the decoding process to simply retrieve and apply pre-computed data, significantly reducing decoding complexity while maintaining high storage efficiency.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11552651B2Hierarchical point cloud compression

Publication Date: 2023.01.10 APPLE INC

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. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values used to correct predicted attribute values are included in the compressed attribute information file. Attribute values are predicted based, at least in part, on attribute values of neighboring points and distances between a particular point for whom an attribute value is being predicted and the neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. A decoder follows a similar prediction process as an encoder and corrects predicted values using attribute correction values included in a compressed attribute information file.