A fundamental problem in depth from defocus is the measurement of relative defocus between images. We propose a class of broadband operators that, when used together, provide invariance t o scene texture and produce accurate and dense depth maps. Since the operators are broadband, a small number of them are sufficient for depth estimation of scenes with complex textural properties. Experiments are conducted on both synthetic and real scenes t o evaluate the performance of the proposed operators. The depth detection gain error is less than I%, irrespective of texture frequency. Depth accuracy is found t o be 0.5 -1.2% of the distance of the object, from the imaging optics.
Abstract-Structures of dynamic scenes can only be recovered using a real-time range sensor. Depth from defocus offers an effective solution to fast and dense range estimation. However, accurate depth estimation requires theoretical and practical solutions to a variety of problems including recovery of textureless surfaces, precise blur estimation, and magnification variations caused by defocusing. Both textured and textureless surfaces are recovered using an illumination pattern that is projected via the same optical path used to acquire images. The illumination pattern is optimized to maximize accuracy and spatial resolution in computed depth. The relative blurring in two images is computed using a narrow-band linear operator that is designed by considering all the optical, sensing, and computational elements of the depth from defocus system. Defocus invariant magnification is achieved by the use of an additional aperture in the imaging optics. A prototype focus range sensor has been developed that has a workspace of 1 cubic foot and produces up to 512 x 480 depth estimates at 30 Hz with an average RMS error of 0.2%. Several experimental results are included to demonstrate the performance of the sensor.
Abstract-Structures of dynamic scenes can only be recovered using a real-time range sensor. Depth from defocus offers an effective solution to fast and dense range estimation. However, accurate depth estimation requires theoretical and practical solutions to a variety of problems including recovery of textureless surfaces, precise blur estimation, and magnification variations caused by defocusing. Both textured and textureless surfaces are recovered using an illumination pattern that is projected via the same optical path used to acquire images. The illumination pattern is optimized to maximize accuracy and spatial resolution in computed depth. The relative blurring in two images is computed using a narrow-band linear operator that is designed by considering all the optical, sensing, and computational elements of the depth from defocus system. Defocus invariant magnification is achieved by the use of an additional aperture in the imaging optics. A prototype focus range sensor has been developed that has a workspace of 1 cubic foot and produces up to 512 x 480 depth estimates at 30 Hz with an average RMS error of 0.2%. Several experimental results are included to demonstrate the performance of the sensor.
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