3D Point Cloud Encoding Using Morton Code Prediction
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Solution Overview
Problem
Current methods for encoding three-dimensional data are inefficient, particularly in terms of coding efficiency, when dealing with large datasets such as point clouds, which are essential for applications like autonomous vehicles and map information.
Innovation Solution
A three-dimensional data encoding method that selects candidate points for predicting attribute information based on their proximity and Morton codes, calculating prediction residuals, and generating bitstreams to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed using conventional encoding methods, then data transmission efficiency is improved, but coding efficiency deteriorates due to the massive amount of three-dimensional data
Solution Approach 1:
The patent segments the point cloud data into multiple regions based on spatial coordinates, and processes each region independently using parallel encoding. This divides the massive data processing task into smaller manageable units, improving both coding efficiency and transmission efficiency without overwhelming the encoding system
Solution Approach 2:
The patent introduces Morton codes to encode the three-dimensional spatial positions of points, transforming spatial relationship information into a one-dimensional sequence that can be efficiently processed. This dimensional transformation enables effective compression while maintaining spatial coherence, resolving the contradiction between compression ratio and decoding accuracy
2Reliability
If all three-dimensional points are encoded with high precision, then data integrity is maintained, but data size increases requiring more compression
Solution Approach 1:
The patent applies different encoding precision levels to different spatial regions based on their importance. Critical regions maintain high precision encoding to preserve data integrity, while less critical regions use compressed encoding. This local differentiation maintains overall data integrity while significantly reducing total data size
Solution Approach 2:
The patent uses predicted values from neighboring points as approximations for actual point attributes. Instead of encoding every point's complete attribute information, the system encodes only the residual difference from the predicted value, dramatically reducing data size while maintaining reconstruction accuracy through the copying of spatial patterns
Data Source
AI summary
A three-dimensional data encoding method includes: selecting, as one or more candidates for calculating a predicted value of attribute information of a first three-dimensional point, one or more three-dimensional points from a plurality of three-dimensional points, the one or more three-dimensional points being closest to the first three-dimensional point; calculating a predicted value using pieces of attribute information of the one or more three-dimensional points selected as the one or more candidates; calculating a prediction residual that is a difference between the attribute information of the first three-dimensional point and the predicted value calculated; and generating a bitstream, based on the prediction residual. In the selecting, when the plurality of three-dimensional points include three-dimensional points equidistant from the first three-dimensional point, the one or more candidates are selected based on a first Morton code of the first three-dimensional point.