自己運動を利用した探索空間削減による高密度視差推定
Dense Disparity Estimation in Ego-motion Reduced Search Space
前フレームの視差マップを自己運動で現フレームに変換しカルマンフィルタの予測として用いることで、視差探索空間とちらつきを抑えつつ精度と計算効率を向上させる手法を提案し、KITTIで検証した。
著者: Luka Fućek, Ivan Marković, Igor Cvišić, Ivan Petrović
分類: cs.RO
原文アブストラクト
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.
