1. A method for vision field computing, comprising steps of: (1) forming a sampling system for a multi-view dynamic scene comprising N cameras discretely distributed in the dynamic scene for acquiring a vision field corresponding thereto, wherein view angles of the cameras entirely cover the dynamic scene, and the N is larger than 2; (2) controlling the cameras in the sampling system for the dynamic scene to perform spatial interleaved sampling, temporal interleaved exposure sampling and exposure-variant sampling in view-time 2D vision field subspace of the dynamic scene to acquire sampling information of the vision field subspaces in the dynamic scene from the view angles of the cameras; (3) performing spatial intersection to the sampling information in the view subspace of the dynamic scene and/or temporal intersection to the sampling information in the time subspace of the dynamic scene to reconstruct a dynamic scene geometry model corresponding to the multi-view dynamic scene; (4) performing silhouette back projection to the view angles of the cameras where the sampling information of the dynamic scene is obtained based on the dynamic scene geometry model to obtain silhouette motion constraints for the view angles of the cameras; (5) performing temporal decoupling for motion de-blurring to images in the sampling information with the silhouette motion constraints; and (6) reconstructing a dynamic scene 3D model corresponding to the multi-view dynamic scene with a resolution larger than nominal resolution of each camera by a 3D reconstructing algorithm.