Point cloud registration with numerous applications

including 3D modeling

object recognition

scene understanding

3D shape detection

and craniofacial reconstruction

is a significant and active research topic in computer vision.A 3D scanner can only obtain the partial 3D point cloud of one object associated with a single coordinate system from one viewpoint.The 3D point clouds captured from different viewpoints must be transformed into a common coordinate system according to rigid transformations to reconstruct the overall 3D shape.The 3D point cloud registration aims to compute the rigid transformation between 3D point clouds and automatically obtain the complete 3D shape of the object.Skull registration is an important step in craniofacial reconstruction.The correctness of its registration will directly affect the result of craniofacial reconstruction.Skull registration is the process of searching for one or more reference skulls from the existing skull database that is most similar to an unknown skull.The face of the reference skull can be used as the reference face of the unknown skull to provide a possible basis for craniofacial reconstruction.Thus far

most of the skull registration methods are feature-based method that contains two methods

namely

global and local feature-based methods.The extraction of feature descriptors is extremely important for registration.The global feature descriptor performs excellent discrimination for complete object representation

whereas the local feature descriptor is more robust against noise and outliers.The local feature descriptor is more suitable for the skull model registration than the global feature descriptor because of the complexity of skull point cloud model.Among the local feature descriptors

3D point-based descriptor has been widely applied to represent a partial object because of its excellent generalization.The 3D point-based descriptor encodes the information of neighboring points of an interest point in a compact and distinctive approach.Then

the 3D points with similar local features can be identified from cluttered scenes through descriptor matching. A skull point cloud model registration method based on coarse-to-fine local features is proposed in this study to improve the accuracy and convergence rate of skull registration.The registration method of the skull point cloud model consists of two steps

namely

initial and fine registrations.In the initial registration

local feature representation and alignment are important steps in recovering a coarse rigid transformation.An accurate initial transformation can improve registration efficiency and reduce the optimization error in the following fine registration step.In the fine registration

an improved iterative closest point (ICP) algorithm is used to complete accurate registration.The detailed registration method is described as follows：First

the local features of the skull point cloud model

which consist of local depth and deviation angle between normal and point cloud density

are extracted.Second

the coarse registration of skull point cloud is achieved through local feature extraction

correspondence of local feature calculation

and outlier elimination.The skulls are coarsely aligned through the coarse registration step.Finally

an improved ICP algorithm that integrates Gaussian probability model and active iterative coefficient to the ICP algorithm is used to complete the fine registration of the skull point cloud model

obtaining an accurate registration of the skull point cloud model. In the experiment

the public point cloud models (i.e.

rabbit and dragon point cloud models) and the skull point cloud model are used to complete the registration.The experimental results showed that the point cloud registration algorithm based on local features can complete accurate registration of either of the point cloud models.Moreover

the registration results are especially remarkable for the skull point cloud model.In the fine registration stage of the skull point cloud model

the registration accuracy and convergence rate increased by 30% and 60%

respectively

compared with the ICP algorithm.The registration accuracy is almost the same and the convergence rate has increased by 50%

compared with the probability ICP algorithm. The point cloud registration method based on local features can register public point cloud model accurately

as well as achieve extremely remarkable registration results for skull point cloud model.Thus

the point cloud registration method is an effective skull registration algorithm with high accuracy and convergence rate.