Page 1 of 10

Journal for Studies in Management and Planning

Available at

http://edupediapublications.org/journals/index.php/JSMaP/

e-ISSN: 2395-0463

Volume 02 Issue 12

December 2016

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 92

Continuous Depth Map Reconstruction from Light Fields

Meghavath Kavitha, & Mrs.G.Satya Prabha

1PG Scholar, Department of ECE, SLC's Institute of Engineering and Technology, Piglipur Village, Hayathnagar

Mandal, Near Ramoji Film City, Ranga Reddy District, Hyderabad, Telangana

2Assistant Professor, Department of ECE, SLC's Institute of Engineering and Technology, Piglipur Village,

Hayathnagar Mandal, Near Ramoji Film City, Ranga Reddy District, Hyderabad, Telangana

Abstract In this paper, we investigate how

the recently emerged photography

technology—the light field—can benefit

depth map estimation, a challenging

computer vision problem. A novel

framework is proposed to reconstruct

continuous depth maps from light field data.

Unlike many traditional methods for the

stereo matching problem, the proposed

method does not need to quantize the depth

range. By making use of the structure

information amongst the densely sampled

views in light field data, we can obtain

dense and relatively reliable local

estimations. Starting from initial

estimations, we go on to propose an

optimization method based on solving a

sparse linear system iteratively with a

conjugate gradient method. Two different

affinity matrices for the linear system are

employed to balance the efficiency and

quality of the optimization. Then, a depth- assisted segmentation method is introduced

so that different segments can employ

different affinity matrices. Experiment

results on both synthetic and real light fields

demonstrate that our continuous results are

more accurate, efficient, and able to

preserve more details compared with

discrete approaches.

I. INTRODUCTION

Depth map reconstruction, also known as

disparity estimation, is a traditional

challenging computer vision task which has

been studied for more than three decades.

The conventional way to get depth values is

from stereo images. Scharstein and Szeliski

gave a good survey of this topic in Most

methods nowadays solve the problem by

minimizing an energy function, which

usually consists of a data term and a

smoothness term. The two most popular

models for the energy functions are the

Markov Random Fields model (MRFs) and

variational approaches while the formulation

of the data term and the smoothness term

can be different. For stereo matching, a lot

of previous work shows that energy

Page 2 of 10

Journal for Studies in Management and Planning

Available at

http://edupediapublications.org/journals/index.php/JSMaP/

e-ISSN: 2395-0463

Volume 02 Issue 12

December 2016

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 93

functions with non-convex terms model the

problem better, although in fact only an

approximate optimization can be found. In

this case, the recovered depth values are not

continuous but discrete in the depth range. A

drawback of the discrete methods is that the

time and memory cost of the algorithm is

related to the number of quantized levels.

When the level is not high

The newly developed technology the light

field has brought new possibilities in

reconstruction of depth maps. Compared

with traditional image data, a light field

contains not only accumulated colour

intensities but also some information about

ray directions. In general, the light field data

can be seen as a set of photos captured from

densely and regularly placed cameras. When

the views are dense enough, there are some

interesting properties that make light fields

different from traditional multi-view data.

One of the properties that has been studied

to solve traditional computer vision

problems is the fact that the projected points

from one 3D point onto different views

correspond to a line in the so called

epipolarplane image (EPI). The slope of this

line is related to the depth of the point in the

space, which transforms the problem of

depth estimation into line detection on EPIs.

To estimate the orientation of the lines on

EPIs, one idea is to try out all different

orientations: the one with the least colour

variance along the line is most likely to give

the correct depth value. Several methods

have been developed based on this point;

different methods use different ways to

measure the colour variance. Kim et al.

employed a modified

Parzen window estimation with an

Epanechenikov kernel Tao et al., on the

other hand, used the standard deviation

estimation. Defocus is another clue that can

be used for depth. Instead of using the

colour variance, researchers try to find out

by how much angle the EPIs need to be

sheared to make the interest point in focus.

Defocus and colour variance are used

together to find the depth . An alternative

approach proposed by Wanner and

Goldluecke is to use a structure tensor to

estimate the slope of the lines on EPIs.

Unlike the other methods, it does not need to

try out different hypothetical depth values to

find the optimal one, but at once provides an

estimation as well as a certainty level from

one structure tensor operation. In this work,

we use the estimation from the structure

tensor as a starting point, followed by a

refinement step by examining the colour

Page 3 of 10

Journal for Studies in Management and Planning

Available at

http://edupediapublications.org/journals/index.php/JSMaP/

e-ISSN: 2395-0463

Volume 02 Issue 12

December 2016

Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 94

correspondence along the detected line from

the structure tensor. The initial estimations

from light fields are denser and more

reliable compared with those from

traditional stereo images, which also makes

the optimization step different. However,

some works still follow a similar

optimization method as with stereo

matching, such as MRFs in and functional

lifting in .These methods have to discretize

the depth values, so lose the advantages of

the dense and reliable initial estimations.

Wanner and Goldluecke showed in

Fig. 1. Two-plane parametrization of 4D

light fields. that a simple denoising filter can

generate comparable results with the

discrete global optimization,

since the filter keeps the continuous depth

values. In Kim et al.’s work by applying the

local method iteratively on the EPIs with

different resolutions, global estimation can

be avoided. Only a median filter is applied

to eliminate outliers. With the

aforementioned initial estimation methods,

the depth values usually come with certainty

levels. Therefore the optimization problem

is in essence to propagate the reliable

estimations to other parts. In this work, we

explore an optimization method, given by

solving a linear system, which generates a

smooth and globally optimized result.

II. RELATED WORK

The light field concept was originally

defined by physicists who interpreted the

flow of light as a field. It is a plenoptic

function which describes the amount of

light, also known as radiance, travelling

towards every direction through every point

in the space. However, to measure the

radiance of the light at every location

towards every direction is not feasible in

practice. The capture of light fields in fact

relies on sampling the radiance in space and

reconstructing the plenoptic function. The

4D light field was first proposed and later

widely used in light field analysis. We adopt

the two-plane parametrization of 4D light

fields and denote it as L(x, y, s, t), as shown

in Figure 1. Under this parametrization, a

4D light field can be seen as a 2D array of

perspective views, where (s, t) can be seen

as the index of different views and (x, y) are

spatial coordinates within each view

C:\Users\Admin\Desktop\f1.png