Point Cloud Coding Using Azimuthal Metrics for LIDAR Node Eligibility
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Solution Overview
Problem
Conventional point cloud coding techniques, particularly in Geometry-based Point Cloud Compression (G-PCC), face inefficiencies in determining the eligibility of nodes for direct coding modes due to reliance on parent and neighbor node occupancy information, and lack consideration of azimuthal direction in eligibility criteria, which is crucial for LIDAR captured data.
Innovation Solution
Determine the capturing laser for each node based on its elevation and azimuthal angles, and assess if the node is passed by only one laser beam in both elevation and azimuthal directions, using specific criteria to enhance the eligibility conditions for direct coding modes like IDCM and angular/azimuthal modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional eligibility criteria based on parent and neighbor node occupancy are used, then the coding process is simple, but the coding efficiency is insufficient
Solution Approach 1:
The patent extends the eligibility determination from traditional 2D occupancy-based criteria to a 3D spatial analysis by incorporating azimuthal angle information. This dimensional expansion allows the system to consider not just whether nodes are occupied, but also the angular direction from which points are captured, enabling more accurate identification of isolated points and improving coding efficiency for LIDAR data.
Solution Approach 2:
The patent introduces new parameters (azimuthal angle, capturing laser identification) to the eligibility determination process. By changing the parameter set from simple occupancy counts to include angular and directional information, the system achieves more precise eligibility assessment while maintaining computational feasibility through structured parameter processing.
2Productivity
If azimuthal direction is not considered in eligibility criteria, then the coding process is simpler, but the coding efficiency deteriorates for LIDAR captured data
Solution Approach 1:
The patent enables the coding system to self-adapt to LIDAR data characteristics by automatically identifying capturing lasers and computing azimuthal angles for each node. The system serves itself by extracting relevant angular information directly from the point cloud data without requiring external intervention, thereby optimizing eligibility determination for LIDAR-specific patterns.
Solution Approach 2:
The patent performs preliminary computation of azimuthal angles and capturing laser identification before the eligibility determination process. By pre-calculating these angular parameters and storing them for reference, the system reduces the computational burden during actual eligibility assessment, making the enhanced coding process more efficient.
3Productivity
If isolated points are not accurately identified, then the coding process is simpler, but the compression performance is reduced
Solution Approach 1:
The patent replaces traditional mechanical/occupancy-based point identification with an angular-metric approach. Instead of relying solely on spatial occupancy patterns, the system uses azimuthal angle calculations and capturing laser information to identify isolated points, substituting a more sophisticated angular analysis mechanism for simpler occupancy counting.
Solution Approach 2:
The patent introduces azimuthal angle metric values as an intermediary parameter between the raw point cloud data and the eligibility determination. This intermediary angular information serves as a mediator that bridges the gap between simple occupancy data and complex compression requirements, enabling accurate isolated point identification without directly complexifying the entire coding process.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for point cloud coding. A method for point cloud coding is proposed. The method comprises: determining, during a conversion between a current frame of a point cloud sequence and a bitstream of the point cloud sequence, a plurality of azimuthal angle metric values of a plurality of key points associated with a node of the current frame, the node representing a spatial partition of the current frame; determining a prediction of azimuthal angle metric value of the node; determining a context of entropy coding for a plane position of at least one axis associated with the node based on the plurality of azimuthal angle metric values and the prediction of azimuthal angle metric value; and performing the conversion based on the context of entropy coding.