Range unfolding for Time-of-Flight depth cameras

Choi, Ouk; Lim, Hwasup; Kang, Byongmin; Kim, Yong Sun; Lee, Keechang; Kim, James D. K.; Kim, Chang-Yeong

doi:10.1109/icip.2010.5651383

Cited by 19 publications

(27 citation statements)

References 7 publications

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“…We compared the results of our algorithm to that of Choi [17] and Crabb [2]. Over the entire data set, we observed an average of 94.1% pixels labeled correctly from our method, compared to 84.3% for Choi and 89.7% for Crabb.…”

Section: Comparison To Previous Methodsmentioning

confidence: 82%

“…The first two columns (green) compare the best estimate using the intensity model by itself, without enforcement of spatial coherence. Columns 3-4 intensity (red) show the performance using the Markov random field approach of [2], the first uses an intensity model by Choi [17] and the next uses an intensity model without normal estimation and discontinuity cost considering only . Columns 5-7 (orange) enforce spatial coherence by NLCA, shown with selected variations in distance metric.…”

Section: Algorithm Efficiencymentioning

confidence: 98%

“…McClure et al [16] use depth edges to initially segment the scene before analyzing each segment to determine whether it falls into the unambiguous range based on average intensity, using a manually set threshold. Choi et al [17], using a corrected intensity image [18], apply an EM optimization to classify each pixel as being within or outside the unambiguous range, which feeds the data term for a Markov random field optimization by graph cuts.…”

Section: Related Workmentioning

confidence: 99%

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Fast single-frequency time-of-flight range imaging

Crabb

Manduchi

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper proposes a solution to the 2-D phase unwrapping problem, inherent to time-of-flight range sensing technology due to the cyclic nature of phase. Our method uses a single frequency capture period to improve frame rate and decrease the presence of motion artifacts encountered in multiple frequency solutions. We present an illumination model that considers intensity image and estimates of the surface normal in addition to the phase image. Considering the number of phase wrap as the 'label', the likelihood of each label is estimated at each pixel, and support for the labeling is shared between pixels throughout the image by Non-Local Cost Aggregation. Comparative experimental results confirm the effectiveness of the proposed approach.

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Section: Comparison To Previous Methodsmentioning

confidence: 82%

Section: Algorithm Efficiencymentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

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Fast single-frequency time-of-flight range imaging

Crabb

Manduchi

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…[19][20][21][22] The methods using a single depth map rely on the assumption that the scene consists of objects with similar reflectivities 14,16,18 or on the assumption that the depth discontinuities between adjacent pixels coincide with the transitions of the number of wrappings from n to either n + 1 or n − 1. 15,17 Since the methods require only a single depth map, they suffer less from motion artifacts, enabling their potential applications in dynamic situations.…”

Section: Related Workmentioning

confidence: 99%

“…Previous phase unwrapping methods can be classified into two groups according to the number of input depth maps: those using a single depth map [14][15][16][17][18] and those using multi-frequency depth maps. [19][20][21][22] The methods using a single depth map rely on the assumption that the scene consists of objects with similar reflectivities 14,16,18 or on the assumption that the depth discontinuities between adjacent pixels coincide with the transitions of the number of wrappings from n to either n + 1 or n − 1.…”

Section: Related Workmentioning

confidence: 99%

Wide range time-of-flight camera: design, analysis, and simulation

Choi

2013

Three-Dimensional Image Processing (3DIP) and Applications 2013

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Time-of-flight cameras measure the distances to scene points by emitting and detecting a modulated infrared light signal. The modulation frequency of the signal determines a certain maximum range within which the measured distance is unambiguous. If the actual distance to a scene point is longer than the maximum range, the measured distance suffers from phase wrapping, which makes the measured distance to be shorter than its actual distance by an unknown multiple of the maximum range. This paper proposes a time-of-flight camera, which is capable of restoring the actual distance by simultaneously emitting light signals of different modulation frequencies and detecting them separately in different regions of the sensor. We analyze the noise characteristic of the camera, and acquire simulated depth maps using a commercially available time-of-flight camera, reflecting the increased amount of noise due to the use of dual-frequency signals. We finally propose a phase unwrapping method for restoring the actual distances from such a dual-frequency depth map. Through experiments, we demonstrate that the proposed method is capable of extending the maximum range to at least twice, with high success rates.

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Consumer Depth Cameras and Applications

Lee

2013

Emerging Technologies for 3D Video

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Range unfolding for Time-of-Flight depth cameras

Cited by 19 publications

References 7 publications

Fast single-frequency time-of-flight range imaging

Fast single-frequency time-of-flight range imaging

Wide range time-of-flight camera: design, analysis, and simulation

Consumer Depth Cameras and Applications

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