Point Cloud Quantization via Exponential Bit Partitioning
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Solution Overview
Problem
The storage and transmission of large point cloud data sets, captured by sensors like LIDAR systems, are costly and time-consuming due to their extensive size, which limits their real-time applications.
Innovation Solution
A method involving a computer system that encodes point cloud data using a tree data structure, partitions the data, quantizes the partitions using an exponential function for the quantization step size, and generates a data structure for efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is stored and transmitted in its original format, then data accuracy is preserved, but storage cost and transmission time increase significantly
Solution Approach 1:
The patent applies parameter changes by quantizing point cloud coordinates using exponential functions with adjustable parameters. The quantization step size is determined by an exponential function of a quantization parameter, allowing dynamic adjustment of precision levels. This enables the system to reduce data size and transmission time while maintaining acceptable accuracy by optimizing the quantization parameter based on application requirements.
2Productivity
If point cloud data is quantized to reduce data size, then storage and transmission efficiency improve, but data precision decreases
Solution Approach 1:
The patent uses parameter changes by implementing an exponential quantization function where the quantization step size is determined by an exponential function of a quantization parameter. This allows flexible control over the trade-off between data size and precision. By adjusting the quantization parameter, the system can optimize storage efficiency while maintaining acceptable precision levels for the specific application.
Solution Approach 2:
The patent applies dynamics by making the quantization parameter adjustable and adaptable. The system can dynamically select different quantization parameters based on the specific point cloud data characteristics and application requirements, enabling optimal balance between compression ratio and precision reconstruction for different scenarios.
3Measurement precision
If complex quantization algorithms are used to maintain precision, then data accuracy is preserved, but processing complexity increases
Solution Approach 1:
The patent simplifies processing complexity by using a parameter-based exponential quantization function rather than complex algorithms. The quantization step size is directly computed from an exponential function of a single quantization parameter, making the process computationally efficient while maintaining precision control through parameter adjustment rather than algorithmic complexity.
Data Source
AI summary
An example method includes receiving a plurality of points that represent a point cloud; representing a position of the point in each dimension of a three-dimensional space as a sequence of bits, where the position of the point is encoded according to a tree data structure; partitioning at least one of the sequences of bits into a first portion of bits and a second portion of bits; quantizing each of the second portions of bits according to a quantization step size, where the quantization step size is determined according to an exponential function having a quantization parameter value as an input and the quantization step size as an output; and generating a data structure representing the point cloud and including the quantized second portions of bits.