3D Point Cloud Segmentation for Scalable Object Geometry Mapping
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Solution Overview
Problem
Traditional methods for detecting and mapping objects, such as poles, are non-scalable and expensive, requiring field inspection or manual annotation, and often fail to provide accurate location and geometric features necessary for applications like autonomous driving and drone navigation.
Innovation Solution
A method using deep learning and heuristics for object segmentation from point cloud data, involving a neural network model, clustering module, and geometric fitting module to identify and fit predetermined shapes to objects, enhancing accuracy and efficiency in object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional field inspection or manual annotation methods are used for object detection, then measurement precision can be maintained, but productivity is severely limited and costs increase
Solution Approach 1:
The patent replaces manual field inspection and mechanical annotation processes with an automated deep learning system. A neural network model processes point cloud data to automatically detect and classify objects, eliminating the need for human annotators to physically visit sites or manually label data, thereby dramatically improving productivity while maintaining detection accuracy through learned patterns from training data
Solution Approach 2:
The patent creates a virtual copy of the physical environment through point cloud data generated from LiDAR or other sensing systems. This digital representation allows automated analysis and object detection without requiring physical field inspection, enabling rapid processing of geographic regions while preserving measurement precision through systematic data analysis
2Productivity
If automated detection techniques are used to improve productivity, then detection speed increases, but measurement precision deteriorates due to lack of manual verification
Solution Approach 1:
The patent implements a feedback mechanism where the neural network model is trained on labeled data and continuously improves its detection accuracy. The system processes point cloud data, generates predictions, and refines its performance through backpropagation and loss minimization, ensuring that automated detection maintains high precision while achieving rapid processing speeds
Solution Approach 2:
The patent performs preliminary action by pre-training the neural network model on extensive labeled datasets before deployment. This pre-learning phase enables the model to recognize patterns and features accurately, so that during actual operation, high-precision detection is achieved automatically without requiring real-time manual verification, thus maintaining both speed and accuracy
3Measurement precision
If complex deep learning models are deployed to improve object detection accuracy, then measurement precision improves, but device complexity and computational resources increase
Solution Approach 1:
The patent segments the object detection task into distinct processing stages: point cloud data acquisition, neural network feature extraction, object classification, and geometric parameter estimation. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining high detection accuracy through specialized processing at each stage
Solution Approach 2:
The patent transforms the 3D point cloud data into a more manageable representation suitable for neural network processing. By converting spatial coordinates into feature vectors and probability maps, the system reduces computational complexity while preserving essential geometric information needed for accurate object detection and classification
4Reliability
If manual annotation is used to ensure data quality, then reliability of training data improves, but loss of time increases significantly
Solution Approach 1:
The patent implements self-service by using the neural network to automatically generate annotations for training data. The system processes point cloud data and produces labeled outputs that can be used to re-train or fine-tune the model, creating a self-sustaining cycle that eliminates manual annotation while maintaining data reliability through automated consistency and scalability
Data Source
AI summary
Segmentation of three dimensional objects may be implemented using a neural network model, a clustering module, a factorization module, and a geometric fitting module. The neural network model is configured to analyze point cloud data for a geographic region and assign probability values outputted from the neural network to points in the point cloud data. The clustering module is configured to group a subset of the probability values based on relative locations of the assigned points in the point cloud data. The factorization module is configured to factor a matrix with the subset of the clustered probability values to assign a line for a three dimensional object of the geographic region. The geometric fitting module is configured to fit at least one predetermined shape for the three dimensional object to the point cloud data based at least on the assigned line.