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.