Dense Disparity Estimation in Ego-motion Reduced Search Space (CROSBI ID 646175)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Fućek, Luka ; Marković, Ivan ; Cvišić, Igor ; Petrović, Ivan
engleski
Dense Disparity Estimation in Ego-motion Reduced Search Space
Depth estimation from stereo images remains a challenge even though studied for decades. The KITTI benchmark shows that the state-of-the-art solutions offer accurate depth estimation, but are still computationally complex and often require a GPU or FPGA implementation. In this paper we aim at increasing the accuracy of depth map estimation and reducing the computational complexity by using information from previous frames. We propose to transform the disparity map of the previous frame into the current frame, relying on the estimated ego-motion, and use this map as the prediction for the Kalman filter in the disparity space. Then, we update the predicted disparity map using the newly matched one. This way we reduce disparity search space and flickering between consecutive frames, thus increasing the computational efficiency of the algorithm. In the end, we validate the proposed approach on real-world data from the KITTI benchmark suite and show that the proposed algorithm yields more accurate results, while at the same time reducing the disparity search space.
Stereo Vision ; Visual odometry ; Disparity estimation
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Podaci o prilogu
2017.
objavljeno
Podaci o matičnoj publikaciji
IFAC World Congress
Podaci o skupu
IFAC World Congress
predavanje
09.07.2017-14.07.2017
Toulouse, Francuska