A 3D reconstruction from real-time stereoscopic images using GPU

birdsowlSoftware and s/w Development

Dec 2, 2013 (3 years and 6 months ago)

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A 3D reconstruction from real
-
time
stereoscopic images using GPU

GOMEZ
-
BALDERAS, Jose
-
Ernesto, GIPSA
-
lab,

Jose
-
Ernesto.Gomez
-
Balderas@gipsa
-
lab.grenoble
-
inp.fr

HOUZET, Dominique, GIPSA
-
lab,

Dominique.Houzet@gipsa
-
lab.fr

Abstract

We propose 3D reconstruction method that uses a Graphics Processors Unit (GPU) and a disparity map from block matching algori
thm

(BM).

Context






Strategy

Our algorithm uses two stereoscopic video sequences like inputs and then it processes the two stereoscopic images using a GPU

an
d then we can
visualize a 3D reconstruction in real
-
time.

Methods





Grenoble Images Parole Signal Automatique

UMR CNRS 5216


Grenoble Campus

38400 Saint Martin d’Hères
-

FRANCE

Recent

trends

show

that

there

is

a

high

demand

of

3
D

imaging

in
:



Media

and

entertainment,



Defense

and

Security,



Architecture

and

Engineering
.


3
D

technology

is

being

implemented

in

various

objects

as

it

provides

a

more

realistic

view

than

2
D
:


Machine

vision


Image

segmentation

for

object

recognition


Defense

and

security

via

its

usage

in

simulation


Facial

identification

and

target

detection

a)
Capture Images and Color to Grey Conversion :
color
stereo images pair on RGB space are converted into
grey space images pair.


b)
Sobel Filter and Rectified Stereo Images:
we have
reduced our search in 1D using the epipolar geometry
(
el=er
) of the two images.

c)
Stereoscopic Block Matching Algorithm in GPU
On
CUDA each thread performs the computation to obtain
the disparity map
dmB.

d)
Reproject

disparity map to 3D:
a point in 2D can be
reprojected into 3D dimensions given their coordinates and
the camera intrinsic matrix.


e)
3D

reconstruction visualization of point clouds:
we
calculate the 3D coordinates of each point using the
disparity map
dmB
and we use a cloud point structure,
PC(i)={Xi, Yi, Zi, Ri, Gi, Bi}
to visualize in real
-
time.

Results

The computer we used in experiments is equipped with
an Intel Core i7 3.07GHz, 5GB memory.


Conclusions

Experimental results show a speedup factor (4x faster) of our system
in contrast to CPU system. In addition, the achieved speedup shows
the importance of parallel algorithms and computing architectures in
GPGPU. With real
-
time performance, our system is suitable for
practical applications.

CPU

Intel Core i7
3.07GHz

FPS

GPU

NVIDIA
GeForce
GTX 285

FPS


Speed

Factor

GPU

NVIDIA

Quadro

4000

FPS


Speed

Factor

IUJW_Left

IUJW_Right

129

291

2x

413

3x

Jamie2_L

Jamie2_R


98

290

3x

409

4x