A 3D reconstruction from real-time stereoscopic images using GPU
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A 3D reconstruction from real-time stereoscopic images using GPU GOMEZ-BALDERAS, Jose-Ernesto, GIPSA-lab, [email protected] inp.fr HOUZET, Dominique, GIPSA-lab, [email protected] Abstract We propose 3D reconstruction method that uses a Graphics Processors Unit (GPU) and a disparity map from block matching algorithm (BM). Context Strategy Our algorithm uses two stereoscopic video sequences like inputs and then it processes the two stereoscopic images using a GPU and 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 3D imaging in: •Media and entertainment, •Defense and Security, •Architecture and Engineering. 3D technology is being implemented in various objects as it provides a more realistic view than 2D: •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_Lef t IUJW_Rig ht 129 291 2x 413 3x Jamie2_L Jamie2_R 98 290 3x 409 4x
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A 3D reconstruction from real-time stereoscopic images using GPU. GOMEZ-BALDERAS, Jose-Ernesto, GIPSA-lab, [email protected] HOUZET, Dominique, GIPSA-lab, [email protected]. Abstract - PowerPoint PPT Presentation
Transcript of A 3D reconstruction from real-time stereoscopic images using GPU
A 3D reconstruction from real-time stereoscopic images using GPU
GOMEZ-BALDERAS, Jose-Ernesto, GIPSA-lab, [email protected]
HOUZET, Dominique, GIPSA-lab, [email protected]
Abstract
We propose 3D reconstruction method that uses a Graphics Processors Unit (GPU) and a disparity map from block matching algorithm (BM).
Context
Strategy
Our algorithm uses two stereoscopic video sequences like inputs and then it processes the two stereoscopic images using a GPU and then we can visualize a 3D reconstruction in real-time.
Methods
Grenoble Images Parole Signal AutomatiqueUMR CNRS 5216 – Grenoble Campus38400 Saint Martin d’Hères - FRANCE
Recent trends show that there is a high demand of 3D imaging in: •Media and entertainment, •Defense and Security, •Architecture and Engineering.
3D technology is being implemented in various objects as it provides a more realistic view than 2D:•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.
CPUIntel Core i7
3.07GHzFPS
GPUNVIDIA GeForce GTX 285
FPS
SpeedFactor
GPUNVIDIAQuadro
4000FPS
SpeedFactor
IUJW_LeftIUJW_Right
129 291 2x 413 3x
Jamie2_LJamie2_R
98 290 3x 409 4x
