Iterative Depth Estimation Model Optimization Using Radar Feedback
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Solution Overview
Problem
Current machine learning algorithms struggle to provide accurate depth information, resulting in errors between predicted and actual distances in depth estimation.
Innovation Solution
A method for optimizing a depth estimation model by obtaining an initial model, performing an optimization process using accurate depth information from a radar device, and iteratively refining the model until it meets predetermined requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current machine learning algorithms are used for depth estimation, then the model can be trained and deployed, but the accuracy of depth information is insufficient resulting in errors between predicted and actual distances
Solution Approach 1:
The patent implements feedback by using the radar device to obtain actual depth information and using this feedback to iteratively optimize the depth estimation model. The loss function is constructed based on the difference between predicted depth from the model and actual depth from radar, and this feedback loop continues until the model meets preset accuracy requirements, thereby resolving the accuracy-reliability contradiction
Solution Approach 2:
The patent replaces the unreliable machine learning-based depth estimation with radar-based electromagnetic wave measurement for obtaining ground truth depth information. The radar device uses electromagnetic wave reflection to measure actual distance, substituting the mechanical/optical system with a more reliable electromagnetic measurement system for validation purposes
Data Source
AI summary
This application provides a method for optimizing a depth estimation model. The method includes obtaining a video of an object and capturing a first image and a second image from the video. An initial depth estimation model is obtained. An updated depth estimation model is obtained by performing an optimization process on the initial depth estimation model, and the optimization process is repeatedly performed on the updated depth estimation model. Once the updated depth estimation model meets predetermined requirements, the updated depth estimation model meeting predetermined requirements is determined as a target depth estimation model.