Minimal operator set for passive depth from defocus

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 to 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 to evaluate the performance of the proposed operators. The depth detection gain error is less than I%, irrespective of texture frequency. Depth

• Type: Conference
• Year: 1996
• Where: CVPR
• Authors: Masahiro Watanabe, Shree K. Nayar
• Keywords: Broadband Operators, Complex Textural Properties, Computer Vision, CVPR 1996, Dense Depth Maps, Depth Detection Gain, Depth Estimation