Global Scaling for Point Cloud Position Values
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Solution Overview
Problem
Current point cloud compression technologies face challenges in efficiently encoding and decoding point cloud data, particularly in scaling and quantization, which can lead to violations of bounding box constraints and inefficiencies in data representation.
Innovation Solution
The proposed solution involves a method for globally scaling point cloud data by determining a global scaling factor and applying it to all points in a frame, ensuring that position values remain within the corresponding bounding box, and encoding this scaling information efficiently using a combination of power of two and refinement values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If local scaling is applied to point cloud data, then the representation accuracy of specific regions is improved, but the complexity of the encoding process increases and bounding box constraints may be violated
Solution Approach 1:
The patent applies local scaling by dividing the point cloud into multiple regions (e.g., tiles or slices) and applying different scaling factors to each region. This allows high-precision representation in specific areas of interest while using coarser scaling in less important areas, thus improving overall representation accuracy without uniformly increasing complexity across the entire data set.
Solution Approach 2:
The point cloud data is segmented into multiple independent regions that can be processed separately with different scaling parameters. This segmentation enables the encoder to apply appropriate scaling strategies to each segment, reducing the overall encoding complexity compared to processing the entire point cloud with a single complex scaling model.
2Productivity
If scaling factors are applied to compress point cloud data, then the compression efficiency is improved, but the bounding box constraints may be violated
Solution Approach 1:
The patent performs preliminary calculations to determine scaling factors that will maintain points within bounding box constraints before applying compression. By pre-computing valid scaling ranges and selecting factors that guarantee constraint satisfaction, the system achieves high compression efficiency without violating bounding box requirements during the actual encoding process.
Solution Approach 2:
The encoding process incorporates feedback mechanisms where the encoder monitors whether scaled points remain within bounding box constraints and adjusts scaling factors accordingly. This feedback loop ensures that compression efficiency is optimized while maintaining reliability by preventing constraint violations through iterative refinement of scaling parameters.
3Measurement precision
If more bits are used to specify scaling factors, then the precision of scaling is improved, but the bit rate increases
Solution Approach 1:
The patent dynamically changes the precision parameter (number of bits) for scaling factors based on the characteristics of the point cloud data and the desired compression level. For regions or data sets where high scaling precision is critical, more bits are allocated, while for other regions, fewer bits suffice. This adaptive parameter adjustment optimizes the balance between scaling precision and bit rate, preventing unnecessary increase in overall bit rate.
Data Source
AI summary
An example device for decoding point cloud data includes: a memory configured to store point cloud data; and one or more processors implemented in circuitry and configured to: decode a frame of the point cloud data including a plurality of points, each of the points being associated with position values defining a respective position of the point; determine a global scaling factor for the frame; and scale the position values of each of the points by the global scaling factor. The scaling may be clipped to prevent the points exceeding the boundaries of a corresponding bounding box including respective points.