A three-dimensional geometry and eigencomponent reconstruction method and apparatus based on light and shadow optimization. The method comprises: obtaining photographing of a three-dimensional color point cloud time series by means of an RGBD camera (S101); acquiring matching point pairs between a three-dimensional depth point cloud and vertexes of a reconstructed model, and obtaining a point pair set (S102); establishing a joint energy function based on eigendecomposition according to matching point pairs and a current view angle color image, and solving non-rigid motion position transformation parameters of each vertex on the reconstruction model (S103); solving the energy function to obtain a deformation transformation matrix of vertexes of a surface model and eigencomponents of each item on an image (S104); and deforming the geometry of a previous frame three-dimensional model according to a solved result, and completing and updating the geometry and eigencomponents of a current frame model to realize three-dimensional geometry and eigencomponent reconstruction (S105). The method can improve the robustness of dynamic object tracking and deformation under the conditions of sparse viewpoints and a single viewpoint, can realize accurate solving, has low requirements for devices, and has a broad prospect.
THREE-DIMENSIONAL GEOMETRY AND EIGENCOMPONENT RECONSTRUCTION METHOD AND DEVICE BASED ON LIGHT AND SHADOW OPTIMIZATION