Abstract:
Although many recent stereo vision algorithms have been able to create disparity maps with high accuracy,
because of the sequential nature it is difficult to adopt them for real time applications. Biologically motivated
algorithms involving Gabor filters demonstrate inherent parallelism and could be effectively implemented in parallel
hardware such as Graphics Processing Units(GPUs). We present a real time stereo vision algorithm based on
Gabor filters which effectively use the memory hierarchy and the threading resources of the Graphics Processing
Unit(GPU). Since the 2D filtering process is a critical activity which takes upto 50% of the total time to create the
disparity map, we evaluate the GPU implementation of three filtering methods. Using the optimal filtering method
out of them, we were able to achieve a frame rate of 76 fps for a 512x512 image stream on a NVIDIA GTX 480
GPU, and a I70x speed-up compared to the conventional CPU based implementation.
