3D Point Cloud Semantic Segmentation for Faster Accurate Labeling
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Solution Overview
Problem
Conventional methods for labeling 3D image data are laborious, repetitive, and time-consuming, as they require manual pixel-level labeling, which is difficult due to the granular and context-dependent nature of the data.
Innovation Solution
A deep artificial neural network is trained to generate semantically-segmented 3D point clouds by subdividing the data and processing labeled features to update its weights, enabling automatic pixel-level identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pixel-level labeling is used, then labeling accuracy can be achieved, but the process becomes laborious, repetitive, and time-consuming
Solution Approach 1:
The system performs automatic semantic segmentation where the deep learning model independently processes 3D point cloud data to generate labels without requiring manual human intervention for each pixel, thereby achieving both accuracy and efficiency through automated self-labeling
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated deep learning-based semantic segmentation system that processes 3D point cloud data algorithmically, substituting human manual operations with computational processing to eliminate time-consuming manual work while maintaining labeling quality
2Loss of information
If manual pixel-level labeling is used, then detailed categorical information can be obtained, but the granular nature of the data makes it difficult to understand out of context
Solution Approach 1:
The patent applies segmentation by dividing the 3D point cloud data into meaningful semantic categories through deep learning processing, organizing granular pixel-level information into structured semantic segments that preserve detailed categorical information while improving interpretability through hierarchical organization
Solution Approach 2:
The system transforms 2D point cloud data into 3D semantic representations, adding a dimensional transformation that contextualizes granular data points within three-dimensional spatial relationships, making the information more interpretable while retaining complete categorical details
3Ease of operation
If conventional labeling tools are used, then user control over labeling is maintained, but the process becomes repetitive and inefficient for large datasets
Solution Approach 1:
The deep learning model performs automatic semantic segmentation independently, serving itself to generate labels without requiring repetitive manual user operations, thereby dramatically improving productivity while maintaining quality through algorithmic consistency
Solution Approach 2:
The system changes the operational parameters from manual interactive labeling to automated batch processing, transforming the efficiency metric by processing large datasets through computational algorithms rather than sequential human operations, achieving high productivity while maintaining user control through configurable model parameters
Data Source
AI summary
A computer-implemented method of training a deep artificial neural network includes receiving a three-dimensional point cloud and training the deep artificial neural network by subdividing the three-dimensional point cloud, and updating weights of the deep artificial neural network. A computing system includes a processor; and a memory having stored thereon computer-executable instructions that, when executed by the processor, cause the computing system to receive a three-dimensional point cloud and train the deep artificial neural network by subdividing the three-dimensional point cloud, and updating weights of the deep artificial neural network. In yet another aspect, a non-transitory computer-readable medium includes computer-executable instructions that when executed, cause a computer to receive a three-dimensional point cloud and train the deep artificial neural network by subdividing the three-dimensional point cloud, and updating weights of the deep artificial neural network.