Enhanced VoxNet 3D Model for Point Cloud Classification
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Solution Overview
Problem
Conventional 3D neural network algorithms for analyzing lidar point cloud data face limitations such as poor performance, low accuracy, and complex feature padding processes, which hinder effective classification and increase the complexity of data analysis.
Innovation Solution
The proposed neural network point cloud data analyzing method employs an enhanced VoxNet 3D model, which includes a specific architecture with multiple hidden units, pooling layers, and batch-normalization layers, to stabilize and improve the performance of the neural network, thereby enhancing the accuracy of point cloud data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 3D neural network algorithms are used to analyze point cloud data, then the analysis can be performed, but the accuracy and performance are poor
Solution Approach 1:
The patent segments the point cloud data processing into multiple specialized hidden units (first hidden unit for local features, second hidden unit for global features, third hidden unit for feature fusion) with distinct functions. Each unit contains specific layer configurations optimized for its purpose, dividing the complex classification task into manageable segments that collectively improve accuracy and reliability
Solution Approach 2:
The patent employs a composite neural network architecture combining multiple types of layers (convolutional layers, batch normalization layers, activation layers, pooling layers, dropout layers, fully connected layers) within each hidden unit. This composite structure integrates the strengths of different layer types to achieve both high accuracy and stable performance in point cloud classification
2Measurement precision
If feature padding is conducted to lower fitting problems, then the classification performance can be improved, but the process becomes complex
Solution Approach 1:
The patent implements self-service through dropout layers and batch normalization layers that automatically adjust and normalize features during training without requiring manual preprocessing. The network adapts to varying input distributions dynamically, eliminating the need for complex external feature padding operations while maintaining or improving classification accuracy
3Productivity
If the neural network model is simplified to reduce complexity, then the training speed increases, but the accuracy decreases
Solution Approach 1:
The patent applies local quality by configuring different hidden units with specialized layer structures optimized for their specific functions. The first hidden unit uses a simpler structure for fast local feature extraction, while the second and third hidden units employ more complex structures for global feature extraction and fusion, achieving both speed and accuracy through differentiated local configurations
Solution Approach 2:
The patent performs preliminary action through batch normalization layers and activation layers that are applied early in each hidden unit to normalize inputs and introduce non-linearity. This preliminary processing prepares the data for subsequent layers, enabling faster convergence and higher accuracy without requiring an overall increase in model complexity
Data Source
AI summary
A neural network point cloud data analyzing method includes a data input step and an analyzing step. In the data input step, a processor receives a point cloud data. In the analyzing step, the processor analyzes the point cloud data based on an enhanced VoxNet 3D model. The enhanced VoxNet 3D model includes an input layer, a first hidden unit, a first pooling layer, a 1st to a 3rd second hidden units, a second pooling layer, a third pooling layer and a fourth pooling layer. The input layer is for inputting the point cloud data. The first hidden unit is signally connected to the input layer. The first pooling layer receives an output from a first activation layer. The 1st to the 3rd second hidden units are sequentially connected after the first pooling layer.