**3.2. Experiment 2: terrestrial laser scanner data (1)**

We selected a bridge as a study area in the outdoor environment. We acquired 25.9 million points using a terrestrial laser scanner (GLS-2000, TOPCON) from four viewpoints. Rendered point cloud, depth range image, and point-cloud clustering results are shown in **Figure 11**. The results show that vertical planes, horizontal planes, and natural features are classified clearly. The processing time for the clustering was 6.1 s (Intel Core i7-6567U 3.30 GHz, MATLAB).

**Figure 10.** Point-cloud clustering result: rendered point cloud (left image), filtered point cloud (center image), and clustered point cloud (right image).

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

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

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, MATLAB).

**4. Discussion**

polygon extraction [20], as shown in **Figure 13**.

Our described processing flow in **Figure 2** can be extended from point-cloud clustering to

**Figure 12.** Point-cloud clustering result: colored point cloud (upper image) and clustered point cloud (bottom image).

Point Cloud Clustering Using Panoramic Layered Range Image

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

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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

**Figure 12.** Point-cloud clustering result: colored point cloud (upper image) and clustered point cloud (bottom image).
