Structured Light Calibration Using Differentiable Rendering
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Solution Overview
Problem
Calibrating structured light imaging devices is challenging due to the need for multiple noisy images and the complexity introduced by structured light projectors, making it difficult to achieve stable calibration with fewer images.
Innovation Solution
A method that projects a pattern of structured light onto a target with optical markers, captures an image, determines the device's pose using marker properties, and iteratively refines calibration data using a differentiable renderer and gradient descent algorithm to achieve convergence between rendered and captured images, reducing the number of images required for calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are captured for calibration, then measurement precision and stability are improved, but loss of time and device complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-computing a dense point cloud from the structured light pattern and marker positions before the actual calibration measurement. This pre-computed 3D structure serves as a ready-to-use reference model, eliminating the need to process multiple images during calibration and significantly reducing calibration time while maintaining precision.
Solution Approach 2:
The patent creates a synthetic copy of the calibration target by generating a 3D model from the structured light pattern and marker information. This digital replica is then used for calibration purposes, replacing the need to capture and process multiple physical images of the target, thereby reducing time loss while preserving measurement precision.
2Measurement precision
If multiple images are captured for calibration, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential calibration information from the structured light pattern and marker positions to create a simplified 3D point cloud model. By separating the calibration process into this extracted 3D representation and the actual measurement process, the system reduces the complexity of handling multiple images while maintaining calibration precision.
Solution Approach 2:
The patent generates a digital 3D copy of the calibration target from the structured light pattern, which simplifies the calibration process compared to processing multiple physical images. This 3D model serves as a ready-to-use reference that reduces computational complexity while preserving measurement precision.
3Ease of operation
If traditional camera calibration methods are used, then ease of operation is maintained, but measurement precision deteriorates due to noise and requirement for multiple images
Solution Approach 1:
The patent combines the structured light pattern information with the marker position data to create a composite calibration approach. This hybrid method leverages both the geometric information from markers and the pattern information from structured light, achieving high precision while maintaining ease of operation through a unified calibration process that requires only a single image.
Data Source
AI summary
A method of calibrating a structured light imaging device includes an image sensor and a structured light projector. The method includes projecting, by the structured light projector, a pattern of structured light onto a target, the target including a plurality of optical markers, capturing, by the image sensor, an image of the pattern on the target, determining, using (i) predetermined data specifying properties of the optical markers and (ii) the appearance of the markers in the captured image, a pose of the device relative to the target, and rendering an image of the target and the pattern using the pose of the device and initial calibration data using a 3D model of the observed calibration scene. The method then includes iteratively refining said rendered image until a substantial convergence is achieved between the rendered image and the captured image by adjusting the calibration data.