Point Cloud Sameness Estimation Using Shared Neural Network Weights
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Solution Overview
Problem
Existing techniques for creating 3D models from point cloud data face challenges in accurately evaluating the sameness of data sets obtained under varying measurement conditions, such as direction, position, distance, timing, and device type, leading to inconsistencies in model alignment.
Innovation Solution
A point cloud data sameness estimation system utilizing shared weights between two neural networks to evaluate the sameness of 3D point cloud datasets, with data augmentation processes to simulate different measurement conditions, ensuring accurate sameness evaluation by training on both identical and distinct shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to create 3D models from point cloud data obtained under varying measurement conditions, then the modeling process can be completed, but the evaluation accuracy of sameness deteriorates due to inconsistencies in alignment
Solution Approach 1:
The system segments the evaluation task by using two separate neural networks (first DNN and second DNN) to independently process first point cloud data and second point cloud data respectively. Each network extracts features from data obtained under different measurement conditions, and the sameness evaluation is performed by comparing the extracted features. This segmentation allows the system to handle varying measurement conditions while maintaining evaluation accuracy.
Solution Approach 2:
The system changes the parameter of weight sharing between the two neural networks. By making the weights of the first DNN and second DNN share common values, the system enables the networks to learn consistent feature representations despite processing data under different measurement conditions. This parameter change (weight sharing) directly improves the evaluation accuracy of sameness while maintaining adaptability to varying conditions.
2Adaptability or versatility
If separate neural networks are used to process point cloud data from different measurement conditions, then adaptability to varying conditions is improved, but evaluation accuracy of sameness deteriorates due to independent weight learning
Solution Approach 1:
The system merges the weight parameters of the first neural network and second neural network by making them share common weight values. While the networks remain structurally separate to handle different input data independently, their weight parameters are combined/shared. This merging of weights ensures that both networks learn consistent feature extraction patterns, thereby improving evaluation accuracy while maintaining adaptability to different measurement conditions.
Solution Approach 2:
The shared weights serve a universal function across both neural networks. The same weight parameters are used in both the first DNN and second DNN, making the weight set multi-functional. This universality ensures that the feature extraction capability is consistent across different measurement conditions, improving sameness evaluation accuracy while the networks themselves remain adaptable to their respective inputs.
3Reliability
If data augmentation with deletions and noise is applied during training, then robustness to measurement variations is improved, but training complexity increases
Solution Approach 1:
The system performs preliminary actions by applying data augmentation (deletions and noise addition) to the training data before feeding it to the neural networks. By pre-processing the training data with various degradation operations, the networks learn to be robust to measurement variations in advance. This preliminary action increases reliability while the complexity is managed through efficient implementation of augmentation operations.
Data Source
AI summary
For information about point cloud data, a point cloud data sameness estimation apparatus and a point cloud data sameness estimation system in which accuracy of evaluating sameness is improved are provided. In the present disclosure, a point cloud data sameness estimation apparatus for estimating sameness of objects that are sources of two 3-dimensional point cloud datasets includes a point cloud data acquisition unit configured to acquire first point cloud data and second point cloud data including 3-dimensional point cloud data; a first neural network configured to output a first point cloud data feature, with information about the first point cloud data as an input into the first neural network; a second neural network configured to output a second point cloud data feature, with information about the second point cloud data as an input into the second neural network; and a sameness evaluation unit configured to output an evaluation about sameness of the first point cloud data and the second point cloud data, based on the first point cloud data feature and the second point cloud data feature, wherein a weight is mutually shared by the first neural network and the second neural network.