Abstract
A stereo approach to resolve the occlusion problem in stereo video sequence is introduced. We define a measure to evaluate the reliability of an initial disparity in combination with a left-right consistency check. An initial matching cost volume is computed with an absolute difference-census measure. In the spatial propagation stage, the outlier with a low reliability value is replaced/updated with the reliable disparity information in the support region. Because previous methods establish correspondence on a per-frame basis, they cannot obtain temporally coherent disparity results over a stereo sequence. In order to overcome the occlusion problem in a dynamic situation, we employ the modified codebook with color, disparity, reliability, array of the matching cost, and final access time in a temporal propagation procedure. Experimental results show that the proposed algorithm with general-purpose computing on graphics processing units (GPGPU) provides better performance when applied to disparity maps of real-time indoor/outdoor scenes.
Highlights
IntroductionDense stereo matching is one of the most extensively studied topics in computer vision.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18] It is an effective three-dimensional reconstruction method, since it can usually recover a dense disparity map from a stereo view
Stereo matching algorithms are classified widely into local and global matching methods, In addition, stereo algorithms are described in more detail according to four individual components in stereo matching, matching cost computation, cost aggregation, disparity computation, and disparity refinement.[1]
The proposed method is implemented on a general-purpose computing on graphics processing units (GPGPU) with compute unified device architecture (CUDA) to handle the heavy computational loads of both the stereo matching and the cost refinement.[26]
Summary
Dense stereo matching is one of the most extensively studied topics in computer vision.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18] It is an effective three-dimensional reconstruction method, since it can usually recover a dense disparity map from a stereo view. Kinect sensor using an infrared band captures precise range information, but it is only for indoor use and its operation range is substantially limited. Stereo systems are useful in both indoor/outdoor applications, such as robot navigation and autonomous vehicle control. Most global stereo methods are computationally expensive and involve many parameters, while local stereo methods are generally efficient and easy to implement
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