Camera Pose Refinement for 3D Profilometry Accuracy
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Solution Overview
Problem
Current methods for determining camera pose in profilometry, such as those described in U.S. Pat. No. 7,605,817 and U.S. Publication No. 2009/0295908, do not provide the necessary accuracy for high-precision applications like dental crown production, leading to errors in aggregating three-dimensional information.
Innovation Solution
The implementation of a system that determines the 'True Pose' of a camera by refining the 'Tentative Pose' to 'Rough Pose' using cross-correlation techniques and post-processing, allowing for accurate positional information to combine image data sets and reduce error in three-dimensional profilometry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard pose determination techniques are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary pose estimation using simplified methods during image capture, then refines this estimate through post-processing after all images are captured. This two-stage approach allows using simpler initial methods while achieving high precision through subsequent refinement without requiring complex real-time computation.
Solution Approach 2:
The system uses feedback from multiple image comparisons to iteratively refine the pose estimation. By comparing images and adjusting the pose determination based on consistency across multiple views, the system achieves high precision through iterative refinement rather than relying on a single complex measurement.
2Measurement precision
If real-time pose determination is performed, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs a rough pose estimation in real-time during image capture using simplified algorithms, enabling immediate processing. After all images are captured, a more precise pose determination is performed as a preliminary action before final processing, ensuring high precision without sacrificing real-time capability during capture.
Solution Approach 2:
The pose determination process is segmented into two distinct stages: a fast real-time rough estimation during image capture, and a precise post-processing refinement after all images are captured. This segmentation allows each stage to be optimized for its specific requirements, maintaining both speed and precision.
3Manufacturing precision
If multiple image scenes are aggregated, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary pose estimation and rough alignment during image capture, preparing the data structure in advance. This preliminary organization of data reduces the computational burden during final post-processing, allowing accurate aggregation of multiple image scenes while minimizing time loss.
Solution Approach 2:
The image aggregation process is segmented into real-time rough alignment during capture and final precise aggregation after capture. By separating these operations, the system can perform computationally intensive accurate aggregation without delaying the capture process, optimizing both precision and time efficiency.
Data Source
AI summary
Hardware and software configurations, optionally, for performing profilometry of an object are disclosed. An advantageous imaging device is described. An advantageous approach to determining imager position is also described. Each aspect described may be used independently of the other. Moreover, the teaching may find use in other fields including velocimetry, etc.