Abstract
In this study, an efficient technique for immersive visualization of point cloud data is described and analyzed, and a manual extraction method is proposed. With the advancement of reconstruction technologies, laser scanned point cloud data has gained importance in robotics, 3D model reconstruction, and other computer fields. The point cloud data from the laser scanner has been widely used to reconstruct into a realistic 3D model. The 3D visualization of an unprocessed point cloud data from the laser scanner is often necessary to perform visual analysis, interactive selection, and editing operations in 3D. For the interactive visualization of point cloud data, OpenGL point primitives are used to accelerate for proficient rendering. The manual editing operations are performed using octree-based neighbors within a radius search algorithm. Finally, the implementation and performance comparison between an octree and kd-tree radius search method are evaluated using real-world example datasets.
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