Artificial scenes often contain abundant planar and linear structures, which are essential for various remote sensing tasks. However, most existing plane segmentation methods primarily rely on point features, neglecting the structural and guiding roles of line features. To address this, we propose a unified RANSAC-based framework that integrates point and line features for efficient and robust plane segmentation. It introduces new sampling patterns for points and lines, guided by an adaptive probability model that dynamically adjusts the sampling strategy based on the quality of the generated ...
