A computer-implemented ego motion estimation method comprising: using a camera for consecutively capturing an input image for each of a plurality of time frames, and acquiring a channel image and a depth image as the input image; generating, for an input image segmented into spaces, a scene flow field indicating a scene flow for each of the spaces, said generating comprising calculating, for each of the spaces, a motion vector and a depth vector, and calculating the scene flow based on the motion vector and the depth vector, the scene flow being generated based on the motion vector indicating a two-dimensional movement of the corresponding space and the depth vector indicating a depth-directional movement from a corresponding time frame to a subsequent time frame; clustering the spaces into groups based on the scene flow field; labeling each of the groups using a plurality of labels, each of the groups being labeled as a stationary background or a dynamic object, the labeling being performed on consecutively obtained input images such that a label matching each space has a continuity, said labeling comprising matching a label corresponding to a stationary background to a stationary space in an initial image acquired when the camera is not moving; generating a probability vector map comprising probability vectors for each of the spaces on which clustering is completed, wherein the probability vector for a space indicates a probability of that space belonging to each of the plurality of labels; and estimating ego motion information indicating movement of the camera based on the probability vector map, wherein the estimating comprises using the probability vector map to identify the stationary background from the spaces that have the highest probability with respect to the label corresponding to the stationary background, and calculating the ego motion information based on the identified stationary background. using a camera for consecutively capturing an input image for each of a plurality of time frames, and acquiring a channel image and a depth image as the input image; generating, for an input image segmented into spaces, a scene flow field indicating a scene flow for each of the spaces, said generating comprising calculating, for each of the spaces, a motion vector and a depth vector, and calculating the scene flow based on the motion vector and the depth vector, the scene flow being generated based on the motion vector indicating a two-dimensional movement of the corresponding space and the depth vector indicating a depth-directional movement from a corresponding time frame to a subsequent time frame; clustering the spaces into groups based on the scene flow field; labeling each of the groups using a plurality of labels, each of the groups being labeled as a stationary background or a dynamic object, the labeling being performed on consecutively obtained input images such that a label matching each space has a continuity, said labeling comprising matching a label corresponding to a stationary background to a stationary space in an initial image acquired when the camera is not moving; generating a probability vector map comprising probability vectors for each of the spaces on which clustering is completed, wherein the probability vector for a space indicates a probability of that space belonging to each of the plurality of labels; and estimating ego motion information indicating movement of the camera based on the probability vector map, wherein the estimating comprises using the probability vector map to identify the stationary background from the spaces that have the highest probability with respect to the label corresponding to the stationary background, and calculating the ego motion information based on the identified stationary background. <EOS>