Photo-consistency and multiresolution methods for light field disparity estimation

Abstract

Light fields can be used to generate photorealistic renderings of a scene from novel viewpoints without need for a model of the scene. Reconstruction of a novel view, however, often leads to ghosting artefacts, which can be relieved by correcting for the depth of objects within the scene using disparity compensation. Disparity estimation offers a solution to both better reconstruction and compression of large amounts of data in light fields. In this paper, we present two novel methods of disparity estimation for light fields: a global method based on the idea of photo-consistency and a local method which employs wavelet subbands for initial disparity estimation and Kalman filtering to refine the estimates. Experimental results demonstrate the effectiveness of the two methods as compared to other photo-consistency based disparity estimation techniques.

Light Field Reconstruction Using a Planar Patch Model

“…We compute per pixel disparity values between horizontally and vertically adjacent cameras using the multiresolution approach described in [7]. Let δx i,j and δy i,j respectively denote the horizontal and vertical disparities between two cameras i and j.…”

“…For this reason, we discard patches that cannot be represented using a 4×4 patch, and patches that become oblique to the camera (patches over 80 degrees) because they are very likely to be unreliable. We generated the patch data both for perfect disparity maps found from the scene geometry and estimated disparity maps in [7].…”

“…The reconstruction PSNR and time complexity for all the algorithms Table 1, where N is the number of pixels, C is the number of cameras, and D is the number of depth samples (for the photo-consistency approach). In case of the photo-consistency reconstruction, we maximised the photo-consistency metric described in [7]. For warping and patch based reconstructions, we used disparity maps from scene geometry and estimations using [7].…”

“…In case of the photo-consistency reconstruction, we maximised the photo-consistency metric described in [7]. For warping and patch based reconstructions, we used disparity maps from scene geometry and estimations using [7]. Whilst the PSNRs are comparable, the patch based algorithm produces noticeably sharper and higher quality reconstructions.…”