3D line segment detection for unorganized point clouds from multi-view stereo

This paper presents a fast and reliable approach for detecting 3D line segment on the unorganized point clouds from multi-view stereo. The core idea is to discover weak matching of line segments by re-projecting 3D point to 2D image plane and infer 3D line segment by spatial constraints. On the basis of 2D line segment detector and multi-view stereo, the proposed algorithm firstly re-projects the spatial point clouds into planar set on different camera matrices; then finds the best re-projection line from tentative matched points. Finally, 3D line segment is produced by back-projection after outlier removal. In order to remove the matching errors caused by re-projection, a plane clustering method is implemented. Experimental results show that the approach can obtain satisfactory 3D line detection visually as well as high computational efficiency. The proposed fast line detection can be extended in the application of 3D sketch for large-scale scenes from multiple images.