An integration method for scanned multi-view range images (MRIs) based on local weighted least squares (LWLS) surface fitting

•A maximum likelihood criterion is developed to detect overlapping areas.•A tiny neighborhood is defined to accelerate shifting operation.•A simple rule is designed to identify homogeneous points.•A fast clustering and fusing method is proposed to gather and merge corresponding points.

We present a novel integration method that can fuse registered partially overlapping multi-view range images (MRIs) into a single-layer, smooth and detailed point set surface. A maximum likelihood criterion is developed to detect overlapping points in MRIs. Subsequently, the detected overlapping points are shifted onto a series of piecewise smooth local weighted least squares (LWLS) surfaces to remove bad influence of scanning noises, outliers and large gaps/registration errors. The LWLS surface is fitted in background neighborhood which contains sufficient information to reconstruct local surface accurately. And the shifting operation is done in a concentric tiny neighborhood which contains corresponding overlapping points. Finally, a simple procedure is designed to identify and merge those corresponding overlapping points. The novel method has the advantages of robust to large gaps/registration errors, possessing least squares means and uniform density distribution. Furthermore, the novel method is efficient since only overlapping points are processed and the non-overlapping points are remained as they are. Several state of the art integration methods were employed for comparison study and the experimental results demonstrate the superiority of the novel method.