Three Dimensional Reconstruction from Optical Flow Using Temporal Integration

摘要

Image flow, the apparent motion of brightness patterns on the image plane, can provide important visual information such as distance, shape, surface orientation, and boundaries. It can be determined by either feature tracking or spatio-temporal analysis. The optical flow thus determined can be used to reconstruct the 3-D scene by determining the depth from camera of every point in the scene. However, the optical flow determined by either of the methods mentioned above will be noisy. As a result, the depth information obtained from optical flow can not be successfully used in practical applications such as image segmentation, 3-D reconstruction, path planning, etc. By using temporal integration, the accuracy of both the optical flow and the depth determined from optical flow can be increased. In the work, the authors describe an incremental integration scheme called the running average method to temporally integrate the image flow. They integrate the depth from camera obtained using optical flow determined from gradient based methods, and show that the results of temporal integration are much more useful in practical applications than the results from local edge operators. Finally, they consider an image segmentation example and show the advantages of temporal integration.