Download - 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