Point Cloud Compression Using Hilbert Curve Delta Ordering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data compression methods for point clouds result in poor compression efficiency due to significant variations in delta values, especially when using nearest neighbor approaches, leading to high storage and transmission burdens.
Innovation Solution
The method involves ordering points in a point cloud according to a space-filling curve, such as a Hilbert curve, which groups points close in distance together, reducing variations in delta values and enabling improved compression through delta encoding and entropy encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional data compression methods with nearest neighbor approaches are used, then the compression process is simple, but the compression efficiency is poor due to significant variations in delta values
Solution Approach 1:
The patent applies preliminary action by ordering the point cloud data along a space-filling curve (such as Hilbert or Morton curve) before performing delta encoding. This pre-ordering ensures that points are arranged in a sequence where spatially adjacent points are close to each other in the sequence, thereby minimizing variations in delta values and improving compression efficiency before the actual compression process begins.
Solution Approach 2:
The patent changes the parameter of point ordering from conventional nearest neighbor approach to space-filling curve ordering. This parameter change transforms the sequence in which points are processed, ensuring that points with similar spatial coordinates are grouped together, which reduces the variance of delta values and enables better compression ratios.
2Ease of operation
If points are ordered using nearest neighbor approach, then the ordering process is straightforward, but the delta values exhibit significant variations leading to poor compression
Solution Approach 1:
The patent applies dimensionality change by mapping multi-dimensional point cloud data onto a one-dimensional space-filling curve trajectory. This transformation allows points that are spatially adjacent in 3D space to be sequentially adjacent in 1D order, reducing delta value variations and improving compression ratio while maintaining operational simplicity.
Solution Approach 2:
The patent utilizes the curved, continuous nature of space-filling curves (such as Hilbert or Morton curves) to order points. These curves provide a smooth traversal through the point cloud space, ensuring that points along the curve are spatially proximate, which minimizes delta value variations and improves compression effectiveness.
3Reliability
If large point cloud data is transmitted and stored using conventional methods, then all original data must be preserved, but significant computing and communications resources are consumed
Solution Approach 1:
The patent changes the parameter of data representation by applying space-filling curve ordering followed by delta encoding. This transformation reduces the entropy of the data sequence, allowing for more efficient compression while preserving all original point cloud information. The compressed representation consumes fewer computing and communications resources for storage and transmission.
Data Source
AI summary
Techniques of data compression involve ordering the points of a point cloud according to distance along a space-filling curve. Advantageously, a space-filling curve has the property that points close in distance along the curve are close together in Euclidean space. Thus, differences between points ordered by distance along such a curve, e.g., a Hilbert curve, will be close. When the curve is fractal, i.e., self-similar at all levels, the differences will be small even when the points are very unevenly clustered throughout the point cloud. Such small differences will provide greatly improved compression to the resulting delta-encoded set of points.