Machine Learning Camera Pose Estimation With Iterative Loss Feedback
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Solution Overview
Problem
Conventional deep neural network (DNN) methods for estimating camera pose and depth in images yield lower accuracy compared to manual estimation methods, and existing DNN techniques do not effectively incorporate iterative learning processes to enhance estimation precision.
Innovation Solution
A method involving a first and second machine learning model to predict depth maps and camera poses, followed by iterative learning using gradient descent to refine these predictions, reducing errors through repeated calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a deep neural network is used to estimate camera pose and depth, then the estimation process is simplified and automated, but the estimation accuracy deteriorates compared to manual methods
Solution Approach 1:
The patent implements a feedback mechanism where the predicted camera pose and depth are used to generate a synthesized image, which is then compared with the actual image to compute a loss. This loss is fed back into the neural network to update the parameters iteratively, allowing the system to correct estimation errors and improve accuracy while maintaining automation.
Solution Approach 2:
The patent changes the parameters of the neural network iteratively by inputting gradients of the loss function into the network. This allows the model to adjust its internal parameters dynamically based on the discrepancy between predicted and actual images, thereby improving estimation accuracy without manual intervention.
2Speed
If a single DNN operation is used to estimate camera pose, then the processing speed is fast, but the estimation accuracy is insufficient
Solution Approach 1:
The patent applies the DNN multiple times in an iterative manner, where each iteration refines the camera pose and depth estimation. The process continues until convergence, ensuring that the useful action of estimation is performed continuously rather than once, thereby achieving both speed and accuracy.
Solution Approach 2:
By using the loss function and gradients to feedback into the DNN for iterative refinement, the system maintains fast processing while improving accuracy through multiple passes of estimation and correction.
3Measurement precision
If iterative gradient descent is applied to refine DNN predictions, then the estimation accuracy is improved, but the computational complexity increases
Solution Approach 1:
The iterative gradient descent process uses feedback from the loss function to automatically adjust the DNN parameters. This feedback mechanism streamlines the complexity by providing a clear direction for optimization, reducing the need for manual tuning and simplifying the overall computational process.
Solution Approach 2:
The system performs self-service by automatically generating the loss, computing gradients, and updating its own parameters without external intervention. This self-service approach manages the computational complexity internally, allowing the system to optimize its own performance without adding external computational burden.
Data Source
AI summary
An method comprises predicting a depth map by inputting a first image or a second image paired with the first image to a first machine learning model, predicting relative movement and rotation of an image capturing device using a second machine learning model, and generating a first converted image in which image conversion based on the predicted relative movement and rotation and the depth map is applied to the first image. The method also comprises calculating a loss including an error between the first converted image and the second image, and repeatedly predicting the relative movement and rotation by inputting a gradient of the loss, the first converted image, and the first image and the second image to the second machine learning model, and converging the loss including the error between the new first converted image and the second image.