Single-view Depth Calibration Using Known Geometry
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Solution Overview
Problem
Current stereo depth camera calibration methods require multiple views and feature detection, which increase acquisition time, are mechanically complex, and are compromised by low modulation transfer function of infrared cameras, affecting depth measurement accuracy.
Innovation Solution
A single-view feature-less depth and texture calibration process using a target with known geometry, allowing for efficient computation of intersection points and calibration of intrinsic and extrinsic parameters without relying on feature detection, reducing complexity and increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-view approach with feature detection is used, then calibration accuracy may be improved through multiple measurements, but acquisition time increases and device complexity increases
Solution Approach 1:
The calibration process is segmented into two distinct stages: a rough calibration stage using multi-view feature detection to obtain initial parameters, and a refinement stage using feature-less imaging to achieve final high-precision calibration. This segmentation allows the system to leverage the speed of feature-based methods for initial setup while achieving high accuracy through subsequent refinement without requiring multiple complex views.
Solution Approach 2:
The rough calibration using multi-view feature detection is performed as a preliminary action to establish initial calibration parameters before the refinement process. This preliminary calibration provides a starting point that enables the subsequent feature-less refinement to converge faster and achieve higher precision without requiring extensive acquisition time.
2Measurement precision
If multi-view approach with feature detection is used, then calibration accuracy may be improved through multiple measurements, but device complexity increases
Solution Approach 1:
The calibration system is segmented into two functional modules: a rough calibration module handling multi-view feature detection and a refinement module using feature-less imaging. This segmentation isolates the complex feature detection operations to an initial setup phase, while the main operational phase uses simpler feature-less imaging, thereby reducing overall device complexity while maintaining calibration accuracy.
Solution Approach 2:
The complex feature detection and matching operations are extracted and confined to the rough calibration stage only. The refinement stage extracts only the essential calibration refinement function using feature-less imaging, thereby removing unnecessary complexity from the ongoing calibration process while preserving measurement precision.
3Ease of manufacture
If feature detection is used for calibration, then calibration can be performed with standard targets, but detection quality is compromised by low modulation transfer function of IR camera
Solution Approach 1:
Feature detection using simple calibration targets is performed as a preliminary action during rough calibration only. The subsequent refinement stage uses feature-less imaging that is insensitive to the IR camera's low modulation transfer function, thereby overcoming the detection quality limitation while still benefiting from the simplicity of standard calibration targets in the initial setup.
Solution Approach 2:
The refinement imaging process acts as an intermediary that bridges the gap between simple calibration targets and high-precision calibration requirements. By using feature-less imaging in the refinement stage, the system overcomes the IR camera's limited feature detection capability while maintaining compatibility with standard calibration targets used in the preliminary stage.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach simplifies the calibration process, reduces time, and improves accuracy even with low-quality sensors, enabling precise depth and texture calibration in a single view, thereby enhancing the performance of stereo depth cameras.
Implementation Method 1
a projector configured to project a sequence of light patterns on a target
Implementation Method 2
a first camera configured to capture a sequence of images of the target illuminated with the projected light patterns
Implementation Method 3
reconstruct depth by triangulation... computing intersection points of a single view of captured images with a calculated single view of a target having known geometry
Data Source
AI summary
A method and apparatus for performing a single view depth and texture calibration are described. In one embodiment, the apparatus comprises a calibration unit operable to perform a single view calibration process using a captured single view a target having a plurality of plane geometries having detectable features and being at a single orientation and to generate calibration parameters to calibrate one or more of the projector and multiple cameras using the single view of the target.