The present invention discloses a non-
feature extraction dense SFM three-dimensional
reconstruction method, comprising: inputting n images about a certain
scenario, n≧2; establishing a world coordinate
system consistent with a certain camera coordinate
system; constructing an objective function similar to
optical flow estimation by taking a depth of a three-dimensional
scenario and a camera projection matrix as variables; employing a from coarse to fine
pyramid method; designing an iterative
algorithm to optimize the objective function; outputting depth representing the three-dimensional information of the
scenario and a camera projection matrix representing relative location and
pose information of the camera; and realizing dense projective, similarity or Euclidean reconstruction according to the depth representing the three-dimensional information of the scenario. The present invention can accomplish dense SFM three-dimensional reconstruction with one step. Since
estimation of dense three-dimensional information is achieved by one-step optimization, an optimal solution or at least local optimal solution can be obtained by using the objective function as an index, it is significantly improved over an existing method and has been preliminarily verified by experiments.