Detector Modeling Techniques for Pre-Clinical 3D PET Reconstruction on the GPU
A 3D reconstruction from real-time stereoscopic images using GPU
description
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]
AbstractWe propose 3D reconstruction method that uses a Graphics Processors Unit (GPU) and a disparity map from block matching algorithm (BM).
Context
StrategyOur 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.
ResultsThe 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