INCREMENTAL SEGMENTATION OF LIDAR POINT CLOUDS WITH AN OCTREE-STRUCTURED VOXEL SPACE Miao Wang (miaowang@geomatics.ncku.edu.tw) Yi-Hsing Tseng (tseng@mail.ncku.edu.tw) National Cheng Kung University, Tainan, Taiwan Abstract Lidar (light detection and ranging) data implicitly contains abundant three- dimensional spatial information. The segmentation of lidar point clouds is the key procedure for transforming implicit spatial information into explicit spatial informa- tion. Common criteria used for point cloud segmentation are proximity and coher- ence of point distribution. An effective segmentation algorithm may apply various steps or combinations of criteria depending on the application. This paper proposes a four-step segmentation method for lidar point clouds to deliver incremental segmentation results. Segmentation results of each step can provide the fundamental data for the next step. In the first step, the input point cloud is organised into an octree-structured voxel space, in which the point neighbourhood is established. In the second step, connected voxels which are not empty are grouped to obtain grouped points based on proximity. The third step is a coplanar point segmentation based on both coherence and proximity, which was performed on each point group obtained in the second step. Finally, neighbouring coplanar point groups are merged into ‘‘co-surface’’ point groups based on the criteria of plane connection and intersection. This scheme enables an incremental retrieval and analysis of a large lidar data-set. Experimental results demonstrate the effectiveness of the segmentation algorithm in handling both airborne and terrestrial lidar data. It is anticipated that the incremen- tal segmentation results will be useful for object modelling using lidar data. Keywords: co-surface, incremental segmentation, lidar, octree, point cloud, voxel space Introduction Point clouds, captured with terrestrial or airborne lidar (light detection and ranging), consist of a large number of points distributed as layers corresponding to scanned object surfaces. A point cloud implicitly contains both geometric and radiometric information since the three-dimensional (3D) coordinates and the laser intensity of each lidar point are recorded. Exploring the 3D spatial information of point clouds for various applications has been a significant topic of research (Fujii and Arikawa, 2002; Barnea et al., 2007; Biosca and Lerma, The Photogrammetric Record 26(133): 32–57 (March 2011) DOI: 10.1111/j.1477-9730.2011.00624.x Ó 2011 The Authors. The Photogrammetric Record Ó 2011 The Remote Sensing and Photogrammetry Society and Blackwell Publishing Ltd. Blackwell Publishing Ltd. 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street Malden, MA 02148, USA.