Depth Error Detection Network for 3D Image Reconstruction
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Solution Overview
Problem
Existing methods for reconstructing 3D images from 2D images using machine learning often result in errors in depth information due to information loss during the conversion process, leading to suboptimal quality in reconstructed 3D images.
Innovation Solution
A method and apparatus utilizing a depth error detection network to diagnose and correct errors in estimated depth information by inputting depth information and color images, employing encoders and decoders trained with cross-modal distillation, and a loss function to improve the accuracy of depth information estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth information is estimated using machine learning from 2D images, then 3D image reconstruction can be achieved, but error in depth information occurs due to information loss during conversion
Solution Approach 1:
The patent implements a feedback mechanism by training the depth estimation network using ground truth depth information and then using this trained network to guide the correction process. The system continuously refines depth estimates by comparing predicted values with actual values and adjusting parameters accordingly, creating a closed-loop feedback system that progressively reduces depth information errors.
Solution Approach 2:
The patent introduces an intermediary correction model that acts as a mediator between the initial depth estimation and the final corrected depth information. This correction model takes both the predicted depth map and ground truth data as inputs, processes them through learned transformations, and outputs corrected depth values that compensate for information loss during the 3D to 2D conversion process.
2Measurement precision
If a depth error detection network is used to diagnose and correct depth information errors, then accuracy of depth information is improved, but device complexity increases
Solution Approach 1:
The patent merges the depth estimation network and the correction model into a unified system where both components work together synergistically. The correction model is trained alongside the estimation network using the same ground truth data, allowing both models to share computational resources and training data efficiently. This combined approach achieves high accuracy without requiring completely separate complex systems for estimation and correction.
Data Source
AI summary
A method and apparatus for correcting an error in depth information estimated from a two-dimensional (2D) image are disclosed. The method includes diagnosing an error in depth information by inputting a color image and depth information estimated using the color image to a depth error detection network, and determining enhanced depth information by maintaining or correcting the depth information based on the diagnosed error.