Scene Flow Estimation by Growing Correspondence Seeds

Abstract

A simple seed growing algorithm for estimating scene flow in a stereo setup is presented. Two calibrated and synchronized cameras observe a scene and output a se-quence of image pairs. The algorithm simultaneously com-putes a disparity map between the image pairs and opti-cal flow maps between consecutive images. This, together with calibration data, is an equivalent representation of the 3D scene flow, i.e. a 3D velocity vector is associated with each reconstructed point. The proposed method starts from correspondence seeds and propagates these corre-spondences to their neighborhood. It is accurate for com-plex scenes with large motions and produces temporally-coherent stereo disparity and optical flow results. The al-gorithm is fast due to inherent search space reduction. An explicit comparison with recent methods of spatiotemporal stereo and variational optical and scene flow is provided. 1.