**4. Discussion**

**3.3. Experiment 3: terrestrial laser scanner data (2)**

MATLAB).

point cloud (bottom image).

86 Recent Applications in Data Clustering

We selected a long narrow slope including slopes and stone steps as our study field. We prepared a point cloud acquired from 18 viewpoints over a wide area using a terrestrial laser scanner (VZ-400, RIEGL). Acquired point cloud and point-cloud clustering results are shown in **Figure 12**. The results show that features, such as steps, slopes, rock walls, and trees, are classified clearly. The processing time for the clustering was 11.4 s (Intel Core i7 2.80 GHz,

**Figure 11.** Point-cloud clustering result: colored point cloud (upper image), depth image (center image), and clustered

Our described processing flow in **Figure 2** can be extended from point-cloud clustering to polygon extraction [20], as shown in **Figure 13**.

After the "Normal vector classification in projected image," when a small region has a similar direction with the neighboring region, the small region is merged into the neighboring

seed point. These steps are iterated to close the geometry for the 3D smooth polygon generation. These procedures are applied to each rendered point cloud from arbitrary viewpoints. In indoor navigation and BIM, terrestrial laser scanners and indoor MMSs are used for colored point-cloud acquisition to generate floor maps and 3D data in an indoor environment. **Figure 15** shows the result of polygon extraction from the point cloud used in the first experiment.

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However, missing areas and a non-uniform density of the point cloud would exist in pointcloud acquisition. This issue causes transparent and near-far effects in point-cloud visualization. To avoid these effects, we have developed a spatial interpolation based on point-based rendering in the point-cloud visualization and modeling. Nevertheless, when large missing and occluded areas exist, the spatial interpolation approach is inefficient and ineffectual. Therefore, we focused on a randomized algorithm to quickly find approximate nearest-neighbor matches between image patches for image inpainting [21]. The image inpainting aims to improve image quality with deletion works of scratches and unnecessary objects in an image and reconstruction works of the natural image. That is, scratches and unnecessary objects are replaced by other textures in the image [22], as shown in **Figure 16**. In manual works, these objects are replaced using image retouching software such as Adobe Photoshop. The inpaint-

ing approach is an automated procedure for image retouches.

**Figure 14.** Point tracing.

**Figure 15.** Polygon extraction result.

**Figure 13.** Overall processing flow.

region. Otherwise, the small region is deleted from the clustering result. Boundary points are extracted through "Boundary point extraction" from the clustering result. Moreover, polygons are extracted through "Boundary point tracing." Boundaries of features can be extracted from the refined surfaces in a range image. Moreover, 3D polygons can be extracted with topology estimation using these extracted boundaries in the range image. In this procedure, point tracing connects points in the 3D space along the boundary, as shown in **Figure 14**. Although least squares fitting and polynomial fitting are generally applied for straight and curved line extraction from points, these fitting approaches require a straight-line recognition or curved-line recognition. When the point clouds include noises, RANSAC is a suitable approach for feature estimation. However, the RANSAC also requires the fitting procedure. Thus, tracing based on the region growing is applied to complex geometry extraction, as follows. First, a topology of points is estimated in a range image. Continuous 3D points can be extracted when a polyline or polygon is drawn in a range image. Next, a seed point is selected from the continuous 3D points for point tracing. Then, a possible next point is searched within a candidate area. The candidate area is determined using a 3D vector from the seed point. When a point exists within the candidate area, it is connected to the seed point. Otherwise, the point is assumed to be an outlier. A position of the outlier is corrected to a suitable position using the 3D vector from the seed point. Then, the connected point is assumed as the next

**Figure 14.** Point tracing.

region. Otherwise, the small region is deleted from the clustering result. Boundary points are extracted through "Boundary point extraction" from the clustering result. Moreover, polygons are extracted through "Boundary point tracing." Boundaries of features can be extracted from the refined surfaces in a range image. Moreover, 3D polygons can be extracted with topology estimation using these extracted boundaries in the range image. In this procedure, point tracing connects points in the 3D space along the boundary, as shown in **Figure 14**. Although least squares fitting and polynomial fitting are generally applied for straight and curved line extraction from points, these fitting approaches require a straight-line recognition or curved-line recognition. When the point clouds include noises, RANSAC is a suitable approach for feature estimation. However, the RANSAC also requires the fitting procedure. Thus, tracing based on the region growing is applied to complex geometry extraction, as follows. First, a topology of points is estimated in a range image. Continuous 3D points can be extracted when a polyline or polygon is drawn in a range image. Next, a seed point is selected from the continuous 3D points for point tracing. Then, a possible next point is searched within a candidate area. The candidate area is determined using a 3D vector from the seed point. When a point exists within the candidate area, it is connected to the seed point. Otherwise, the point is assumed to be an outlier. A position of the outlier is corrected to a suitable position using the 3D vector from the seed point. Then, the connected point is assumed as the next

**Figure 13.** Overall processing flow.

88 Recent Applications in Data Clustering

seed point. These steps are iterated to close the geometry for the 3D smooth polygon generation. These procedures are applied to each rendered point cloud from arbitrary viewpoints.

In indoor navigation and BIM, terrestrial laser scanners and indoor MMSs are used for colored point-cloud acquisition to generate floor maps and 3D data in an indoor environment. **Figure 15** shows the result of polygon extraction from the point cloud used in the first experiment.

However, missing areas and a non-uniform density of the point cloud would exist in pointcloud acquisition. This issue causes transparent and near-far effects in point-cloud visualization. To avoid these effects, we have developed a spatial interpolation based on point-based rendering in the point-cloud visualization and modeling. Nevertheless, when large missing and occluded areas exist, the spatial interpolation approach is inefficient and ineffectual. Therefore, we focused on a randomized algorithm to quickly find approximate nearest-neighbor matches between image patches for image inpainting [21]. The image inpainting aims to improve image quality with deletion works of scratches and unnecessary objects in an image and reconstruction works of the natural image. That is, scratches and unnecessary objects are replaced by other textures in the image [22], as shown in **Figure 16**. In manual works, these objects are replaced using image retouching software such as Adobe Photoshop. The inpainting approach is an automated procedure for image retouches.

**Figure 15.** Polygon extraction result.

**References**

2009;**64**(6):575-584

pp. 231-242

WSCG. 2007;**15**(1-3):51-58

Geometry. 2011. pp. 510-518

Sciences. 2003;**XXXIV-5/W10**

Systems (ACM GIS). 2008. p. 8

Sciences. 2010;**XXXVIII**(Part 3A):293-298

Computer Graphics Forum. 2007;**26**(2):214-226

[1] Boyko A, Funkhouser T. Extracting roads from dense point clouds in large scale urban environment. ISPRS Journal of Photogrammetry and Remote Sensing. 2011;**66**(2011):S2-S12 [2] Jiang X, Bunke H. Edge detection in range images based on scan line approximation.

Point Cloud Clustering Using Panoramic Layered Range Image

http://dx.doi.org/10.5772/intechopen.76407

91

[3] Vosselman G, Gorte BGH, Sithole G, Rabbani T. Recognising structure in laser scanning point clouds. In: ISPRS 2004: Proceedings of the ISPRS Working Group VIII/2: Laser

[4] Tsai A, Hsu C, Hong I, Liu W. Plane and boundary extraction from LiDAR data using clustering and convex hull projection. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2010;**XXXVIII**(Part 3A):175-179

[5] Pu S, Vosselman G. Knowledge based reconstruction of building models from terrestrial laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing.

[6] Zhou Q, Neumann U. Fast and extensible building modeling from airborne LiDAR data. In: ACM SIGSPATIAL International Conference on Advances in Geographic Information

[7] Denis E, Burck R, Baillard C. Towards road modelling from terrestrial laser points. International Archives of the Photogrammetry, Remote Sensing and Spatial Information

[8] Schnabel R, Wahl R, Klein R. Efficient RANSAC for point-cloud shape detection.

[9] Shade J, Gortler S, He L, Szeliski R. Layered depth images. In: SIGGRAPH '98. 1998.

[10] Verbree E, Zlatanova S, Dijkman S. Distance-Value-Added Panoramic Images as the

[11] Linsen L, Müller K, Rosenthal P. Splat-based ray tracing of point clouds. Journal of

[12] Nakagawa M. Point cloud clustering for 3D modeling assistance using a panoramic lay-

[13] Chevallier N, Maillot Y. Boundary of a non-uniform point cloud for reconstruction. In: SoCG '11 Proceedings of the Twenty-Seventh Annual Symposium on Computational

[14] Fabio R, From point cloud to surface: The modeling and visualization problem. International Archives of the Photogrammetry, Remote Sensing and Spatial Information

Base Data Model for 3D-GIS. Panoramic Photogrammetry Workshop. 2005

ered range image. Journal of Remote Sensing Technology. 2013;**1**(3):10

Computer Vision and Image Understanding. 1999;**73**(2):183-199

Scanning for Forest and Landscape Assessment; 2004. pp. 33-38

**Figure 16.** Inpainted result: rendered point cloud (left image) and inpainted point cloud (right image).
