3D Point Cloud Layered Encoding for Lower Processing Load

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Solution Overview

Problem

Existing three-dimensional data encoding methods face high processing loads due to the massive amount of data in point cloud representation, necessitating efficient compression techniques.

Innovation Solution

A three-dimensional data encoding method that classifies points into layers and encodes attribute information based on a layered structure, or encodes without reordering in Morton order, reducing processing load by generating bitstreams with or without layer information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If point cloud data is used to represent three-dimensional structure, then data completeness and accuracy are improved, but data volume increases massively requiring compression

Engineering Contradiction:

Improvethree-dimensional data accuracyVSAvoiddata volume

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent segments the point cloud data into multiple layers based on depth distance from the camera. Each layer contains points within a specific depth range, allowing independent processing and compression of each layer. This segmentation reduces the complexity of handling massive point cloud data while preserving the complete three-dimensional structure information.

Inventive Principle:

Principle #1Segmentation

2Productivity

If layered structure classification is applied to three-dimensional points, then encoding efficiency is improved, but processing complexity increases

Engineering Contradiction:

Improveencoding efficiencyVSAvoidprocessing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent performs preliminary classification of three-dimensional points into layers based on depth distance before the encoding process. By pre-organizing the point cloud data into a structured layered format, the subsequent encoding operations become more efficient as they can process each layer independently rather than handling the entire unstructured point cloud at once.

Inventive Principle:

Principle #10Preliminary action

3Loss of information

If Morton order reordering is applied to three-dimensional points, then compression performance is improved, but processing load increases

Engineering Contradiction:

Improvecompression performanceVSAvoidprocessing load

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent applies Morton order reordering locally within each layer rather than globally across the entire point cloud. This local reordering approach maintains the compression benefits of Morton ordering by grouping spatially adjacent points within each layer, while reducing the overall processing load by limiting the reordering scope to individual layers instead of the complete dataset.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250260819A1Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

Publication Date: 2025.08.14 PANASONIC INTELLECTUAL PROPERTY CORP OF AMERICA

AI summary

A three-dimensional data encoding method includes: (i) in a first case where a layered structure is generated by classifying three-dimensional points into layers: encoding attribute information for the three-dimensional points based on the layered structure; and generating a bitstream including layer information utilized for the generation of the layered structure; and (ii) in a second case where the three-dimensional points are not classified: encoding attribute information for the three-dimensional points; and generating a bitstream not including the layer information.