Robust Matching of 3D CAD Models to Multiple Views
Nowadays, multi-cameras are ubiquitous in our world, because of the fact that they are able to provide much more information than a single camera does. As the camera prices decrease, people are extensively benefiting from using large amount of cameras. Many applications such as augmented reality, video surveillance, 3D reconstruction and industrial inspection already use multiple cameras. The recent research predicts that such applications will continue to utilize many cameras. Additionally, the market research shows that such a generic measuring system has a lot of use, especially in Automobile Industry, White-Goods Industry, Electronics Industry and so on.
One of the biggest problems involved in using multi-camera setups is robust 3D measurement of CAD parts, where environment and process dependent noise is significant. Such systems require projective registration of a CAD model to multiview camera images. Until now, many studies are carried out in order to achieve the task of fitting CAD models to multiple, monochrome photographs. In this work, we will be posing this problem as an ICP-like optimization where the global geometric poses of the individual cad parts are refined from an automatically chosen initial guess. We make use of accurate sub-pixel edges and robust functions in order to be resilient to outliers and corrupted observations. While being straightforward this method greatly enjoys from the fact that the methods used are well-studied and proven to work well under many conditions. Our approach is invariant to the structure of the geometry and sufficiently immune to errors in the initialization. While being extendible and easy to apply, this technique inherently computes the correspondences of the CAD model to the sub-pixel edges, which might further be exploited for recalibration of the measurement system not from a predefined grid, but automatically from an erroneous measurement sample.
Eventually, we perform extensive tests on real data and demonstrate both numerically and visually that the accuracy of the system is even on a globally calibrated and inaccurate system is reasonable for the industrial standards. Last but not least, we discuss the opportunities in this field and how the current measurement systems can be improved to reach the most accurate measurements.
This work is not yet published, but a paper will be available soon.
Below is a sample video:
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Click here for the informal poster of an early stage version .