Neural Network Relative Depth Map Generation
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Solution Overview
Problem
There is a need for fast, accurate, and flexible methods to detect and quantify changes in depth maps, particularly in surveillance of urban development, agriculture, and landscapes, as existing methods are inefficient and inflexible.
Innovation Solution
A method involving neural networks is used to generate relative depth map images by comparing depth maps from different time periods, utilizing satellite and synthetic data to train the network, and generating 3D models for improved accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to detect and quantify depth changes, then the process is simple to implement, but the accuracy and efficiency are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/optical depth measurement systems with an AI-based neural network system that processes satellite imagery. The neural network is trained to automatically detect and quantify depth changes, substituting complex physical measurement infrastructure with computational analysis of remote sensing data.
Solution Approach 2:
The patent uses satellite images as copies or representations of the actual terrain. Instead of direct physical measurement, the system creates digital replicas of the terrain surface from satellite imagery, processes these copies through neural networks, and derives depth information from the processed image data.
2Productivity
If fast depth change detection is implemented, then productivity increases, but measurement precision may be compromised
Solution Approach 1:
The patent performs preliminary training of the neural network using labeled depth change data before actual deployment. This pre-training phase prepares the system in advance, allowing it to rapidly process new satellite imagery with high accuracy without requiring complex real-time computations during the actual detection phase.
Solution Approach 2:
The patent optimizes neural network parameters such as learning rate, batch size, and architecture configuration to achieve the best balance between processing speed and detection accuracy. By carefully tuning these parameters, the system achieves fast inference while maintaining high precision in depth change measurement.
3Adaptability or versatility
If the system is made flexible to handle different regions and time periods, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent develops a universal neural network model that can process satellite imagery from different regions and time periods using the same architecture. The single model is trained on diverse data and can adapt to various terrains, satellites, and time periods without requiring separate specialized systems, achieving multi-functionality through a unified approach.
Solution Approach 2:
The patent uses data augmentation techniques that involve changing parameters such as rotation, scaling, and temporal spacing of training samples. This allows the neural network to learn from varied conditions during training, making the system adaptable to different regions and time periods without increasing structural complexity.
Data Source
AI summary
The present disclosure relates to a method for generating a relative depth map image. The method comprises feeding at least two input images to a neural network, the input images relating to a region of interest at different time periods, the input images being obtained at corresponding arbitrary positions and attitudes with respect to the region of interest, and predicting, using the neural network, the relative depth map image.