3D Point Cloud Subdivision for Parallel Encoding and Selective Decoding
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Solution Overview
Problem
Existing methods for encoding and decoding three-dimensional data are inefficient, leading to prolonged processing times.
Innovation Solution
The method involves dividing three-dimensional data into independent sub-clouds, appending spatial information to each sub-cloud's header, and encoding them separately to allow for parallel processing and selective decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If three-dimensional data is encoded using conventional methods, then the encoding process can be completed, but the processing time becomes excessively long
Solution Approach 1:
The patent divides the three-dimensional point cloud data into multiple independent sub-clouds based on spatial regions. Each sub-cloud can be encoded and decoded independently, enabling parallel processing that significantly reduces the overall encoding time while maintaining the完整性 of the original three-dimensional data structure
Solution Approach 2:
The patent introduces a spatial partitioning dimension to the encoding process by dividing the three-dimensional space into multiple regions. This dimensional approach allows simultaneous processing of multiple data segments in parallel, transforming a sequential encoding operation into a parallel one that reduces processing time
2Loss of time
If three-dimensional data is divided into independent sub-clouds for parallel processing, then processing time is reduced, but the system complexity increases
Solution Approach 1:
The patent segments the three-dimensional data into sub-clouds with associated metadata including spatial information, encoding parameters, and decoding flags. This segmentation enables independent processing of each sub-cloud while maintaining manageable complexity through structured organization of the divided data units
Solution Approach 2:
The patent performs preliminary actions by pre-defining the spatial division structure and encoding parameters for each sub-cloud before the actual encoding process. This preparation allows the encoding system to process sub-clouds independently without requiring complex real-time coordination, thereby reducing operational complexity while enabling parallel processing
3Adaptability or versatility
If conventional encoding methods are used, then the encoding process is simple, but selective decoding capability is lost
Solution Approach 1:
The patent segments the encoded three-dimensional data into multiple independent sub-cloud units, each with its own header containing spatial information and decoding control flags. This segmentation enables the decoding system to selectively decode only the required sub-clouds based on application needs, providing adaptability without requiring complete decoding of the entire data set
Solution Approach 2:
The patent introduces dynamic decoding control through flags in each sub-cloud header that indicate whether a sub-cloud can be decoded independently or requires reference to other sub-clouds. This dynamic control mechanism allows the decoding system to adaptively select and process only the necessary sub-clouds, enhancing versatility while maintaining manageable structural complexity
Data Source
AI summary
A three-dimensional data encoding method includes: dividing three-dimensional points included in three-dimensional data into three-dimensional point sub-clouds including a first three-dimensional point sub-cloud and a second three-dimensional point sub-cloud; appending first information indicating a space of the first three-dimensional point sub-cloud to a header of the first three-dimensional point sub-cloud, and appending second information indicating a space of the second three-dimensional point sub-cloud to a header of the second three-dimensional point sub-cloud; and encoding the first three-dimensional point sub-cloud and the second three-dimensional point sub-cloud so that the first three-dimensional point sub-cloud and the second three-dimensional point sub-cloud are decodable independently of each other.