Aiming at solving the problem that it is easy to fall into the local optimum when using RGB-D data to perform 3D point cloud registration， a 3D point cloud registration method based on a multi-dimensional-feature PVDAC descriptor is proposed. Firstly， the key-points of the two-dimensional data are extracted through the ORB feature detection algorithm. Secondly， the gray features of the key-points in 2D， the local pixel value distances， point cloud normal angles， and curvature features of the key-points in 3D are calculated， respectively. Thirdly， the 2D feature and 3D feature are combined to generate a new PVDAC pixel descriptor， which is used to describe the key-points to achieve the coarse registration of the 3D point cloud. Finally， the fine registration of the 3D point cloud is completed based on the ICP algorithm. Experiment results show that overall mean square error of this method is about 0.05 m2 when registering a point cloud in a large scene， and it reaches a small error of 0.0002 m2 when registering a single-object point cloud in a small scene.