A fast and memory-efficient Discrete Focal Stack Transform for plenoptic sensors

Pérez, Fernando; Juan, Ángel A.; Rodríguez, María Candelaria Gil; Magdaleno, Eduardo

doi:10.1016/j.dsp.2014.12.007

Cited by 7 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process yields numerically identical results to building the full 6D scale-slope space (7), but at a fraction of the computational cost. A few efficient methods for computing the focal stack F have been proposed [27], [32]. These generally find at minimum as many layers as there are samples in s or t. Feature detection may not require so many layers, and so we proceed with the more straightforward approach of shifting and summing LF subimages (8), with the understanding that computational savings may be possible for large stack depths.…”

Section: B Simplification Using Focal Stackmentioning

confidence: 99%

LiFF: Light Field Features in Scale and Depth

Dansereau

Girod

Wetzstein

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

Feature detectors and descriptors are key low-level vision tools that many higher-level tasks build on. Unfortunately these fail in the presence of challenging light transport effects including partial occlusion, low contrast, and reflective or refractive surfaces. Building on spatio-angular imaging modalities offered by emerging light field cameras, we introduce a new and computationally efficient 4D light field feature detector and descriptor: LiFF. LiFF is scale invariant and utilizes the full 4D light field to detect features that are robust to changes in perspective. This is particularly useful for structure from motion (SfM) and other tasks that match features across viewpoints of a scene. We demonstrate significantly improved 3D reconstructions via SfM when using LiFF instead of the leading 2D or 4D features, and show that LiFF runs an order of magnitude faster than the leading 4D approach. Finally, LiFF inherently estimates depth for each feature, opening a path for future research in light field-based SfM.

show abstract

Section: B Simplification Using Focal Stackmentioning

confidence: 99%

LiFF: Light Field Features in Scale and Depth

Dansereau

Girod

Wetzstein

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

show abstract

Section: Light Field Depth Estimation and Focal Stack Frameworkmentioning

confidence: 99%

“…Pérez et al [35] proposed a focal stack frequency decomposition algorithm from light field images based on the trigonometric interpolation principle as the discrete focal stack transform. The proposed method in [35] utilizes fast discrete Fourier transform to generate refocus planes in a reasonably fast computational time. The reverse of this transformation in studied in [36] where a focal stack is used to obtain a 4D light field image set using discrete focal stack transform.…”

Section: Light Field Depth Estimation and Focal Stack Frameworkmentioning

confidence: 99%

Total variation-based dense depth from multicamera array

Javidnia

Corcoran

2018

Opt. Eng.

View full text Add to dashboard Cite

Multi-Camera arrays are increasingly employed in both consumer and industrial applications, and various passive techniques are documented to estimate depth from such camera arrays. Current depth estimation methods provide useful estimations of depth in an imaged scene but are often impractical due to significant computational requirements. This paper presents a novel framework that generates a high-quality continuous depth map from multi-camera array/light field cameras. The proposed framework utilizes analysis of the local Epipolar Plane Image (EPI) to initiate the depth estimation process. The estimated depth map is then processed using Total Variation (TV) minimization based on the Fenchel-Rockafellar duality. Evaluation of this method based on a well-known benchmark indicates that the proposed framework performs well in terms of accuracy when compared to the topranked depth estimation methods and a baseline algorithm. The test dataset includes both photorealistic and non-photorealistic scenes. Notably, the computational requirements required to achieve an equivalent accuracy are significantly reduced when compared to the top algorithms. As a consequence, the proposed framework is suitable for deployment in consumer and industrial applications.One of the most common techniques for estimating depth from light field data is to exploit the Epipolar Plane Image (EPI) [16]. This has the advantage of being both simple to execute and fast to compute, but the accuracy is limited by the small camera baseline that is typical of these array cameras and most importantly by the illumination variation while capturing Lambertian scenes. An EPI based approach is employed in this paper to initiate the depth estimation framework. In the same way that depth estimation is performed in simple stereo image pairs, the depth from a light field set is

show abstract

“…with the constant ρ defining the weight of the regularization term. The equivalent KKT-condition to (14) then becomes…”

Section: Regularizationmentioning

confidence: 99%

Richardson-Lucy Deblurring for Moving Light Field Cameras

Dansereau

Eriksson

Leitner

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

View full text Add to dashboard Cite

We generalize Richardson-Lucy (RL) deblurring to 4-D light fields by replacing the convolution steps with light field rendering of motion blur. The method deals correctly with blur caused by 6-degree-of-freedom camera motion in complex 3-D scenes, without performing depth estimation. We introduce a novel regularization term that maintains parallax information in the light field while reducing noise and ringing. We demonstrate the method operating effectively on rendered scenes and scenes captured using an off-theshelf light field camera. An industrial robot arm provides repeatable and known trajectories, allowing us to establish quantitative performance in complex 3-D scenes. Qualitative and quantitative results confirm the effectiveness of the method, including commonly occurring cases for which previously published methods fail. We include mathematical proof that the algorithm converges to the maximumlikelihood estimate of the unblurred scene under Poisson noise. We expect extension to blind methods to be possible following the generalization of 2-D Richardson-Lucy to blind deconvolution.

show abstract

A fast and memory-efficient Discrete Focal Stack Transform for plenoptic sensors

Cited by 7 publications

References 21 publications

LiFF: Light Field Features in Scale and Depth

LiFF: Light Field Features in Scale and Depth

Total variation-based dense depth from multicamera array

Richardson-Lucy Deblurring for Moving Light Field Cameras

Contact Info

Product

Resources

About