Point Cloud Encoding Using Distance-Based Point Ordering
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Solution Overview
Problem
Existing point cloud encoding methods based on octrees are inefficient for sparse data due to high encoding complexity and low encoding efficiency caused by scattered 3D location distributions, leading to increased computational demands.
Innovation Solution
The method involves encoding point cloud data by determining encoding orders based on distances among data points, sequentially adding points to an encoding queue, and encoding them in order to reduce complexity and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If octree-based encoding is used for point cloud data, then encoding structure is simplified, but encoding efficiency deteriorates for sparse data due to high encoding complexity
Solution Approach 1:
The patent changes the encoding parameter from spatial distribution (octree-based) to distance-based ordering. By sorting points according to their distances from a reference point and encoding in that order, the method adapts to sparse data distributions without requiring complex octree structures, thus improving efficiency while maintaining simplicity.
Solution Approach 2:
Instead of dividing space into octrees and processing points based on spatial occupancy, the patent inverts the approach by ordering points based on their distances from a reference point. This inversion of the encoding strategy allows sparse points to be processed efficiently without the overhead of octree node management.
2Adaptability or versatility
If octree-based encoding is used for point cloud data, then spatial division is standardized, but encoding complexity increases for sparse data with scattered 3D locations
Solution Approach 1:
The patent changes the division parameter from fixed spatial octree levels to dynamic distance-based ordering. This allows the encoding to adapt to the actual distribution of points in the data, avoiding the complexity of managing empty octree nodes while maintaining systematic processing of scattered points.
Solution Approach 2:
The patent segments the encoding process into two simple steps: calculating distances from a reference point and sorting points based on those distances. This segmentation avoids the need for complex octree subdivision while achieving systematic processing of sparse, scattered points.
3Stability of the object's composition
If octree-based encoding is used for point cloud data, then hierarchical structure is established, but encoding efficiency decreases due to most nodes being empty
Solution Approach 1:
The patent changes the hierarchical organization from space-based octree levels to distance-based ordering levels. This reorganizes the data structure to reflect the actual distribution of points, eliminating empty nodes and improving encoding efficiency while maintaining a stable, systematic processing order.
Solution Approach 2:
The patent creates a simplified version of the hierarchical structure by replacing the complex octree with a distance-sorted sequence. This copying of the hierarchical concept in a simplified form maintains the benefits of systematic processing while eliminating the inefficiencies of empty nodes.
Data Source
AI summary
A point cloud data encoding method and decoding method, a device, a medium, and a program product are provided, and relate to the field of point cloud application technologies. One method includes obtaining point cloud data, the point cloud data comprising at least two data points; and sequentially encoding data points in the point cloud data according to encoding orders of the data points, to obtain encoded point cloud data corresponding to the point cloud data, wherein the encoding orders of the data points being determined based on distances among the data points. Another method includes obtaining encoded point cloud data, obtaining reference information, the reference information being used for indicating a start reference data point of an encoding queue; and sequentially decoding, based on the reference information and the encoded point cloud data, data points according to the encoding orders of the data points.