Monocular Depth Calibration Using Local Regression Planes
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Solution Overview
Problem
Existing depth estimation techniques using monocular cameras require manual calibration or additional equipment, leading to high effort and cost, and result in inaccurate depth estimation.
Innovation Solution
A depth estimation apparatus and method that calculates a calibration value for each partial region of an image based on the camera's installation position relative to horizontal or vertical planes, using regression planes to adjust depth estimates without manual work or additional equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration work or additional equipment is used to ensure depth estimation accuracy, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The system performs self-calibration by automatically detecting reflection planes in the captured image and computing calibration values without requiring manual intervention or additional calibration equipment. The camera system uses its own captured image data to calibrate depth estimates, making the system self-sufficient.
Solution Approach 2:
The patent replaces manual mechanical calibration processes with automated computational methods. Instead of physically positioning reference charts or using specialized calibration equipment, the system uses image processing algorithms to detect reflection planes and calculate calibration values automatically.
2Measurement precision
If manual calibration work is performed to achieve accurate depth estimation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs calibration automatically as part of the normal depth estimation process rather than requiring a separate preliminary calibration step. The calibration values are computed on-demand using the current image data, eliminating the need for time-consuming manual calibration procedures before actual measurement.
Solution Approach 2:
The automatic self-calibration process eliminates the need for manual calibration operations, allowing the system to perform both calibration and depth estimation in a single integrated workflow, thereby significantly reducing the time required.
3Measurement precision
If retraining machine learning model parameters is performed for calibration, then measurement precision is improved, but use of energy and productivity deteriorate
Solution Approach 1:
Instead of retraining the entire machine learning model parameters globally, the system computes local calibration values specific to each image or scene based on detected reflection planes. This localized approach adjusts depth estimates for current conditions without the heavy computational cost of full model retraining.
Solution Approach 2:
The system changes approach from modifying model parameters (retraining) to adjusting calibration values derived from image analysis. This allows adaptation to different scenes and camera conditions through simple parameter adjustments rather than energy-intensive model retraining.
Data Source
AI summary
A depth estimation apparatus executes a first process of calculating a calibration value for an estimated depth and a second process of calibrating the estimated depth based on the calibration value. The first process includes: specifying a plane area in the image in which a horizontal plane or vertical plane is reflected; setting a plurality of partial regions in the image; calculating a regression plane representing the horizontal plane or the vertical plane for each partial region; and calculating the calibration for each partial region by comparing an installation position of the camera with a position of the camera with respect to the regression plane. The second process includes calibrating the estimated depth for each partial region based on the calibration value corresponding to each partial region.