TY - JOUR
T1 - Depth from Defocus vs. stereo
T2 - How different really are they?
AU - Schechner, Yoav Y.
AU - Kiryati, Nahum
N1 - Funding Information:
The authors wish to thank Rafael Piestun and Joseph Shamir for stimulating discussions. This research was supported in part by the Eshkol Fellowship of the Israeli Ministry of Science, by the Ollendorff Center of the Electrical Engineering Department, Technion, and by the Tel Aviv University Internal Research Fund.
PY - 2000/9
Y1 - 2000/9
N2 - Depth from Focus (DFF) and Depth from Defocus (DFD) methods are theoretically unified with the geometric triangulation principle. Fundamentally, the depth sensitivities of DFF and DFD are not different than those of stereo (or motion) based systems having the same physical dimensions. Contrary to common belief, DFD does not inherently avoid the matching (correspondence) problem. Basically, DFD and DFF do not avoid the occlusion problem any more than triangulation techniques, but they are more stable in the presence of such disruptions. The fundamental advantage of DFF and DFD methods is the two-dimensionality of the aperture, allowing more robust estimation. We analyze the effect of noise in different spatial frequencies, and derive the optimal changes of the focus settings in DFD. These results elucidate the limitations of methods based on depth of field and provide a foundation for fair performance comparison between DFF/DFD and shape from stereo (or motion) algorithms.
AB - Depth from Focus (DFF) and Depth from Defocus (DFD) methods are theoretically unified with the geometric triangulation principle. Fundamentally, the depth sensitivities of DFF and DFD are not different than those of stereo (or motion) based systems having the same physical dimensions. Contrary to common belief, DFD does not inherently avoid the matching (correspondence) problem. Basically, DFD and DFF do not avoid the occlusion problem any more than triangulation techniques, but they are more stable in the presence of such disruptions. The fundamental advantage of DFF and DFD methods is the two-dimensionality of the aperture, allowing more robust estimation. We analyze the effect of noise in different spatial frequencies, and derive the optimal changes of the focus settings in DFD. These results elucidate the limitations of methods based on depth of field and provide a foundation for fair performance comparison between DFF/DFD and shape from stereo (or motion) algorithms.
UR - https://www.scopus.com/pages/publications/0034266691
U2 - 10.1023/A:1008175127327
DO - 10.1023/A:1008175127327
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AN - SCOPUS:0034266691
SN - 0920-5691
VL - 39
SP - 141
EP - 162
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
IS - 2
ER -