Finding Optimal Focusing Distance and Edge Blur Distribution for Weakly Calibrated 3-D Vision
3-D Vision is now a common sensing method frequently used in industrial applications. With the convenience of an uncalibrated system, 3-D reconstruction by a self-calibration technique is possible, but always incomplete or unreliable. This paper presents a novel method to analyze the blur distribution in an image and find the optimal focusing distance so that additional constraints can be used to generate absolute measurement of the models. With the assumption of a Gaussian distribution model of the point spread function, this paper applies two theorems to efficiently compute the defocusing extent on stripe edges. Because the blurring diameter implies the distance from the sensor to the surface, we can upgrade the 3-D map obtained from self-calibration with the known scaling factor. Through theoretical and experimental analysis, we find that not only the technology is feasible, but also both the accuracy and the efficiency are satisfactory.
optimal focusing distance blur distribution 3-d vision shape-from-defocus structured light vision weakly calibrated system