Point Cloud Attribute Encoding with Cartesian Reference Geometry
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Solution Overview
Problem
Encoding efficiency is reduced in polar coordinate systems when calculating predictive values for attribute data from peripheral points due to the increased distance between points, compared to Cartesian coordinate systems.
Innovation Solution
Transforming the coordinate system for geometry data from a polar coordinate system to a Cartesian coordinate system, setting a reference relationship in the Cartesian system to calculate predictive values for attribute data, and encoding the prediction residual.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If polar coordinate system is used for geometry data, then the coordinate system matches LiDAR scanning order, but the distance between peripheral points increases and encoding efficiency decreases
Solution Approach 1:
The patent changes the coordinate system parameter from polar to Cartesian coordinates. This transformation modifies the spatial representation parameters while maintaining the underlying point cloud data, thereby improving encoding efficiency without altering the fundamental LiDAR scanning structure
Solution Approach 2:
The patent transitions from a polar coordinate system (r, θ, φ) to a Cartesian coordinate system (x, y, z), effectively changing the dimensional representation. This dimensionality change allows for better spatial correlation in attribute data while preserving the original point cloud geometry information
2Stability of the object's composition
If polar coordinate system is used, then geometry data aligns with scanning order, but attribute data correlation decreases due to increased point distances
Solution Approach 1:
The patent applies parameter changes by transforming the coordinate system from polar to Cartesian. This changes the spatial reference parameters used for calculating distances and correlations, thereby improving attribute data correlation while maintaining geometry data integrity through the reversible transformation
3Ease of operation
If predictive value is calculated from peripheral points in polar coordinate system, then processing follows scanning order, but prediction accuracy decreases due to larger distances
Solution Approach 1:
The patent changes the coordinate parameter system from polar to Cartesian, which alters how distances between points are calculated. This parameter change improves prediction accuracy by providing better spatial relationships while maintaining the same processing order and peripheral point selection strategy
Data Source
AI summary
The present disclosure relates to an information processing device and method capable of suppressing a reduction in coding efficiency. For a point cloud representing a three-dimensional object as a set of points, a coordinate system for geometry data is transformed from a polar coordinate system to a Cartesian coordinate system, a reference relationship indicating a reference destination used to calculate a predictive value of attribute data of a processing target point is set by using the generated geometry data in the Cartesian coordinate system, a prediction residual that is a difference value between the attribute data of the processing target point and the predictive value calculated based on the set reference relationship is calculated, and the calculated prediction residual is encoded. The present disclosure can be applied to, for example, an information processing device, an encoding device, a decoding device, an electronic device, an information processing method, or a program.