Neural Network 3D Point Cloud Segmentation via User Interaction
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Solution Overview
Problem
Current methods for point cloud segmentation in computer-aided design and engineering systems lack efficiency, robustness, and generalization power, making them inadequate for effectively segmenting 3D point clouds based on user interactions.
Innovation Solution
A computer-implemented method using a neural network that learns to segment 3D point clouds by incorporating specifications of graphical user interactions, such as clicks, strokes, and bounding boxes, allowing for interactive and iterative segmentation without requiring detailed object categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional point cloud segmentation methods are used, then segmentation can be performed, but efficiency, robustness, and generalization power are insufficient
Solution Approach 1:
The patent replaces traditional mechanical segmentation algorithms (like region growing or graph cutting) with a neural network-based machine learning system. The neural network learns from training data to automatically segment point clouds, substituting rule-based mechanical processing with adaptive intelligent processing that improves both efficiency and robustness simultaneously.
Solution Approach 2:
The patent transforms the segmentation problem by changing parameters from fixed algorithmic thresholds to learned neural network weights and biases. By training the network on diverse point cloud data, the system adapts its internal parameters to handle various segmentation scenarios, improving generalization power while maintaining high efficiency through optimized forward propagation.
2Measurement precision
If detailed object categorization is required for segmentation, then segmentation accuracy may improve, but user burden increases and ergonomics deteriorate
Solution Approach 1:
The neural network performs automatic object categorization and segmentation without requiring users to manually specify object classes or provide detailed annotations. The system serves itself by learning from training data what objects to segment and how to distinguish them, eliminating the need for users to have expert knowledge of object categories while maintaining high segmentation accuracy.
Solution Approach 2:
The system performs preliminary learning and feature extraction during the training phase, so that during actual operation, segmentation can be performed directly on raw point cloud data without requiring users to pre-categorize objects. All the complex categorization work is done in advance by the trained neural network, making the user interface simple and ergonomic.
Data Source
AI summary
A computer-implemented method of machine-learning including obtaining a dataset of 3D point clouds. Each 3D point cloud includes at least one object. Each 3D point cloud is equipped with a specification of one or more graphical user-interactions each representing a respective selection operation of a same object in the 3D point cloud. The method further includes teaching, based on the dataset, a neural network configured for segmenting an input 3D point cloud including an object. The segmenting is based on the input 3D point cloud and on a specification of one or more input graphical user-interactions each representing a respective selection operation of the object in the 3D point cloud.