3D Point Cloud Encoding Merging Duplicated Points
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Solution Overview
Problem
Current methods for encoding three-dimensional data, such as point cloud data, face challenges in reducing the data volume of encoded data, which is essential for efficient transmission and storage, especially in applications like autonomous vehicles and infrastructure inspection where bandwidth and storage are limited.
Innovation Solution
A method that combines pieces of point cloud data and generates a bitstream including information about duplicated points, allowing for efficient identification and encoding of these points, thereby reducing the overall data volume by using first and second information to indicate the geometry and belonging point cloud data for each point index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is encoded for transmission and storage, then data volume is reduced, but coding efficiency needs to be improved further
Solution Approach 1:
The patent combines multiple pieces of point cloud data into a single data structure for joint encoding. By merging multiple point cloud datasets and identifying duplicated points across them, the system encodes the combined data once and uses reference information to represent duplicates, thereby improving coding efficiency while reducing overall data volume.
Solution Approach 2:
The patent creates reference copies of duplicated points across multiple point cloud datasets. Instead of storing duplicate point data in each dataset, the system identifies points that appear in multiple datasets and creates reference information that points to the original definition, reducing redundancy while maintaining data integrity.
2Device complexity
If multiple pieces of point cloud data are encoded separately, then processing is simpler, but data volume is larger
Solution Approach 1:
The patent merges multiple separate point cloud data encoding processes into a single combined encoding operation. By processing multiple datasets together and identifying cross-dataset duplications, the system reduces total data volume while the added complexity is managed through systematic combination strategies.
Solution Approach 2:
The patent segments the point cloud data into individual pieces that can be processed separately to identify duplicates, then combines them for efficient encoding. This segmentation approach allows the system to manage complexity by breaking down the problem into manageable parts while still achieving volume reduction through cross-reference identification.
Data Source
AI summary
A three-dimensional data encoding method includes: generating combined point cloud data by combining pieces of point cloud data; and generating a bitstream by encoding the combined point cloud data. The bitstream includes (i) first information indicating a maximum number of duplicated points that are included in each of the pieces of point cloud data and are three-dimensional points having same geometry information, and (ii) pieces of second information corresponding one-to-one with point indexes and each indicating which of the pieces of point cloud data three-dimensional points having a corresponding one of the point indexes belong to, the point indexes being indexes to which values a total number of which is equal to the maximum number are assigned, and being used for identifying duplicated points belonging to same point cloud data.