We propose a workflow for extracting geometric primitives, including linear, planar, and cylindrical objects, from point clouds of the construction site, using a novel segmentation- and recognition-based strategy. The entire point cloud is first organized by an octree-based voxel structure. The proposed voxel- and graph-based segmentation is conducted by aggregating connected adjacent voxels in a fully connected local affinity graph, the weighted edges of which consider their saliencies simultaneously, including the spatial distance, the shape similarity, and the surface connectivity. After the segmentation, an improved efficient RANSAC algorithm is tailored to recognize and extract geometric primitives from segments. The synthetic, laser scanned, and photogrammetric point clouds are tested in our experiments, and qualitative and quantitative results reveal that our method can outperform the representative segmentation algorithms for our application having the precision and recall better than 0.77. It also shows a good performance with a correctness value better than 0.7 in primitive extraction.     «

We propose a workflow for extracting geometric primitives, including linear, planar, and cylindrical objects, from point clouds of the construction site, using a novel segmentation- and recognition-based strategy. The entire point cloud is first organized by an octree-based voxel structure. The proposed voxel- and graph-based segmentation is conducted by aggregating connected adjacent voxels in a fully connected local affinity graph, the weighted edges of which consider their saliencies simultan...     »