Out-of-core outlier removal for large-scale indoor point clouds

Abstract

An accurate coarse-to-fine out-of-core outlier removal method is proposed for large-scale indoor point clouds by mining the geometric shape constraints. In coarse processing stage, a low-resolution point cloud (LPC) is obtained using random downsampling. LPC has the same density distribution as the raw point clouds (RPC), which is important information for outlier removal. The correspondences from the LPC to the RPC are also recorded. The outliers in the LPC are removed via a global threshold. The outliers in the RPC are roughly removed guided by the cleaned LPC. In refinement processing stage, the cleaned LPC is segmented into planar and non-planar segments; and the LPC segmentation is transferred to the RPC. Finally, the outliers in each RPC segment are removed elaborately via a local threshold by exploring the shape information. The experiments show that the proposed method improves the quality of outlier removal results.