Robust Depth Estimation by Fusion of Stereo and Focus Series Acquired with a Camera Array

Frese, Christian; Gheta, Ioana

doi:10.1109/mfi.2006.265618

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…Camera array systems where each camera has a different focus have been constructed, e.g., [13,14,15]. Here, edges of each view are estimated using DfD, with an edge consistence term being used in the multi-view stereo pipeline.…”

Section: Related Workmentioning

confidence: 99%

Depth Recovery from Light Field Using Focal Stack Symmetry

Lin

Chen

Kang

et al. 2015

2015 IEEE International Conference on Computer Vision (ICCV)

118

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We describe a technique to recover depth from a light field (LF) using two proposed features of the LF focal stack. One feature is the property that non-occluding pixels exhibit symmetry along the focal depth dimension centered at the in-focus slice. The other is a data consistency measure based on analysis-by-synthesis, i.e., the difference between the synthesized focal stack given the hypothesized depth map and that from the LF. These terms are used in an iterative optimization framework to extract scene depth. Experimental results on real Lytro and Raytrix data demonstrate that our technique outperforms state-of-the-art solutions and is significantly more robust to noise and undersampling.

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Section: Related Workmentioning

confidence: 99%

Depth Recovery from Light Field Using Focal Stack Symmetry

Lin

Chen

Kang

et al. 2015

2015 IEEE International Conference on Computer Vision (ICCV)

118

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“…Some works (Subbarao et al 1997;Frese and Gheta 2006) have motivated the problem of shape estimation from a cue combination point of view. The authors in Subbarao et al (1997) carry out a sequential process where the shape is first estimated using local DFD and SFF techniques.…”

Section: Relation To Previous Workmentioning

confidence: 99%

“…This shape estimate is used to constrain the stereo correspondence problem and is improved by solving the stereo problem on pin-hole images. Another sequential approach is proposed in Frese and Gheta (2006) where a depth map computed with a contemporary stereo technique on pin hole images is further improved by an SFF technique on a focus series. In their case, the images in focus-series are not coincident (as is required in traditional SFF).…”

Section: Relation To Previous Workmentioning

confidence: 99%

Towards Unrestrained Depth Inference with Coherent Occlusion Filling

Bhavsar

Rajagopalan

2011

Int J Comput Vis

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Traditional depth estimation methods typically exploit the effect of either the variations in internal parameters such as aperture and focus (as in depth from defocus), or variations in extrinsic parameters such as position and orientation of the camera (as in stereo). When operating offthe-shelf (OTS) cameras in a general setting, these parameters influence the depth of field (DOF) and field of view (FOV). While DOF mandates one to deal with defocus blur, a larger FOV necessitates camera motion during image acquisition. As a result, for unfettered operation of an OTS camera, it becomes inevitable to account for pixel motion as well as optical defocus blur in the captured images. We propose a depth estimation framework using calibrated images captured under general camera motion and lens parameter variations. Our formulation seeks to generalize the constrained areas of stereo and shape from defocus (SFD)/focus (SFF) by handling, in tandem, various effects such as focus variation, zoom, parallax and stereo occlusions, all under one roof. One of the associated challenges in such an unrestrained scenario is the problem of removing user-defined foreground occluders in the reference depth map and image (termed inpainting of depth and image). Inpainting is achieved by exploiting the cue from motion parallax to discover (in other images) the correspondence/color information missing in the reference image. Moreover, considering the fact that the observations could be differently blurred, it is important to ensure that the degree of defocus in the missing regions (in the reference image) is coherent with the local neighbours (defocus inpainting).

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“…The reader is referred to [16] for a theory of image registration techniques. For practical examples of fusing image series acquired with a camera array by means of energy functionals, see [17][18][19][20].…”

Section: Image Registrationmentioning

confidence: 99%

“…This is done in each row of the camera array for each two neighboring images, Figure 4(a). Since the disparities for image pairs are not directly comparable (unlike the generalized disparity notion in [17][18][19][20]), the immediate fusion of two disparity maps on the abstraction level of disparities is not possible. In order to obtain a uniform measure, which enables the further fusion process, depth maps are computed from the disparity maps.…”

Section: Depth Estimationmentioning

confidence: 99%

Fusion of combined stereo and spectral series for obtaining 3D information

et al. 2008

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This contribution presents a fusion method for combined stereo and spectral series gained with a camera array with the main purpose of obtaining 3D information. In order to register the images, regions are used as invariant elements with respect to varying gray values. The approach uses their characteristics like size, position and shape for registration. The regions are identified employing the watershed transformation. The fusion problem is modeled with energy functionals that are to be optimized. Using the implemented algorithm, several scenes have been reconstructed. The experimental results show that the proposed method delivers reliable and accurate dense depth maps

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Robust Depth Estimation by Fusion of Stereo and Focus Series Acquired with a Camera Array

Cited by 7 publications

References 22 publications

Depth Recovery from Light Field Using Focal Stack Symmetry

Depth Recovery from Light Field Using Focal Stack Symmetry

Towards Unrestrained Depth Inference with Coherent Occlusion Filling

Fusion of combined stereo and spectral series for obtaining 3D information

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