Point Cloud Feature Extraction Using Sorted 3D Records
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing point cloud data processing methods are complex and require improvement to handle the disordered and irregular nature of point cloud data effectively, often leading to information loss and unsatisfactory processing effects.
Innovation Solution
A method involving sorting point cloud data based on 3D coordinate information, using a one-dimensional convolutional neural network (1D CNN) to extract features, and processing the data efficiently without losing related information, by employing a 1D convolution operation to slide kernels along specific axes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is processed using existing methods, then processing can be performed, but the processing becomes complex and leads to information loss
Solution Approach 1:
The patent applies preliminary action by sorting the point cloud data into a specific order before feeding it into the neural network. This pre-processing step organizes the disordered point cloud data into a structured sequence, which simplifies the subsequent processing steps and prevents information loss during the transformation process.
Solution Approach 2:
The patent segments the point cloud data processing into distinct stages: sorting the point cloud data, extracting features using a neural network, and then processing the extracted features. This segmentation allows each stage to be optimized independently, reducing overall processing complexity while maintaining information integrity.
2Productivity
If point cloud data is processed without sorting, then processing speed may be maintained, but information is lost and processing effectiveness decreases
Solution Approach 1:
The patent performs the sorting operation as a preliminary action before feature extraction. By organizing the point cloud data into a specific order beforehand, the subsequent neural network processing can proceed more efficiently without needing to handle the complexity of unsorted data, thus avoiding information loss while maintaining processing efficiency.
3Adaptability or versatility
If complex processing methods are used for point cloud data, then processing capability increases, but the processing becomes less effective and more complex
Solution Approach 1:
The patent segments the processing pipeline into clear functional blocks: sorting module, feature extraction module (using neural networks), and processing module. This segmentation provides adaptability and versatility in handling different point cloud data scenarios while keeping each module relatively simple and easier to implement.
Solution Approach 2:
The patent changes the parameter order of point cloud data through sorting operations before processing. This parameter transformation simplifies the data structure and makes it more suitable for standard neural network processing, thereby reducing processing complexity while maintaining or enhancing processing capability.
Data Source
AI summary
A processor-implemented method includes obtaining point cloud data comprising records indicating positions and attributes of a plurality of points, sorting the records according to an order of the plurality of points determined based on the positions of the plurality of points, extracting features of the plurality of points, based on a result obtained by applying a feature extraction model to input data based on the sorted records, and processing the point cloud data based on the features of the plurality of points.