Hybrid tree guided Patch Match and quantizing acceleration for multiple views disparity estimation

【摘要】 Existing stereo matching methods cannot guarantee both the computational accuracy and efficiency for the disparity estimation of large-scale or multi-view images. Hybrid tree method can obtain a disparity estimation fast with relatively low accuracy,while Patch Match can give high-precision disparity value with relatively high computational cost. In this work,we propose the Hybrid Tree Guided Patch Match which can calculate the disparity fast and accurate. Firstly,an initial disparity map is estimated by employing hybrid tree cost aggregation,which is used to constrain the label searching range of the Patch Match. Furthermore,a reliable normal searching range for each current normal vector defined on the initial disparity map is calculated to refine the Patch Match. Finally,an effective quantizing acceleration strategy is designed to decrease the matching computational cost of continuous disparity. Experimental results demonstrate that the disparity estimation based on our algorithm is better in binocular image benchmarks such as Middlebury and KITTI. We also provide the disparity estimation results for multi-view stereo in real scenes.更多还原

【Abstract】 Existing stereo matching methods cannot guarantee both the computational accuracy and efficiency for the disparity estimation of large-scale or multi-view images. Hybrid tree method can obtain a disparity estimation fast with relatively low accuracy,while PatchMatch can give high-precision disparity value with relatively high computational cost. In this work,we propose the Hybrid Tree Guided PatchMatch which can calculate the disparity fast and accurate. Firstly,an initial disparity map is estimated by employing hybrid tree cost aggregation,which is used to constrain the label searching range of the Patch Match. Furthermore,a reliable normal searching range for each current normal vector defined on the initial disparity map is calculated to refine the PatchMatch. Finally,an effective quantizing acceleration strategy is designed to decrease the matching computational cost of continuous disparity. Experimental results demonstrate that the disparity estimation based on our algorithm is better in binocular image benchmarks such as Middlebury and KITTI. We also provide the disparity estimation results for multi-view stereo in real scenes.更多还原