Simplification and Layout Extraction Applications

The first information model extracted from accurately captured point cloud data is the section layout. During the process, the point cloud data set is also simplified using a contour based simplification methodology. The simplification strategy removes points adaptively based on the local geometric complexity i.e. more number of points are removed from the planar region and less number of points are removed from geometrically complex regions. Thus, certain elements, features and objects are visible in the simplified scanned scene. The layouts are extracted using a slice of point cloud along a given direction and thus, based on its location, the extracted layout can be used to compute the geometric parameters such as area, volume etc. Another location of layout slice can produce the layouts representing the openings in the walls corresponding to the doors or windows. This information model can be used in the following applications.

7.2.1 Scene visualization

One of the most critical characteristic of 3D scanning of building interiors with multiple objects is that captured point cloud often corresponds to multiple objects with data density having no direct correlation with the geometric complexities of the objects. Here, simple (geometries) as well as complex (freeform) surfaces are captured coherently. Thus, the point cloud is overly populated in planar regions and it does not help to distinguish the features in the scanned scene. Thus, a simplification strategy that can directly focus on important features and extract them directly from the point cloud, is quite beneficial. It not only extract the desired feature (layout in this case), but the simplified data set helps in visualizing the scanned scene accurately. For example, Figure 7.2 shows the scanned scene, a data slice and the simplified data slice.

The original point cloud data (Figure 7.2(a)) is very dense and its visualization does not yield any information about the objects present in the scanned scene. The regions are over- populated in some regions and under-populated in others. The scanner’s settings can be adjusted to capture large number of points to avoid under-populated regions. However, this also increases the number of points at the simplified region and the visualization becomes even worse. It is really difficult to identify the points representing the interior objects. The point cloud simplification strategy proposed in this research helps in reducing the internal points as shown in Figure 7.2. Here the sliced point cloud is decimated to reduce the point cloud in such a way that the point cloud corresponding to internal objects is retained. The resultant slice point cloud (Figure 7.2 (c) and (d)) shows the objects are clearly identifiable and the scene visualization is greatly improved. This facilitates a reliable means to devise effective strategies for CAD modeling and virtual reality application through reverse engineering or pattern recognition. It is to be noted that the approach is purely based on the point cloud where the point cloud is simplified and visualization is improved without generating any intermediate models or derived geometric components.

Figure 7.2 (d) retains points corresponding to the reference spheres, shelves, monitor, telephone and the boundary data point of the tables. This decimated data set can not only be used to identify the number of objects lying in the scanned scene, but can also be used to formulate effective post-processing strategies to model them. The proposed simplification

strategy also extracts layouts from the point cloud data set, which can be used for numerous applications as described below.

7.2.2 Geometric parameter estimation

The layouts extracted from the simplified point cloud data set can be used to estimate the geometric parameters including dimensions, areas and volumes. The extracted shapes provide a direct means of measuring the dimensional details of the length, width or any other dimensional parameter of the scanned objects. Subsequently, other geometric properties can be derived from these dimensional parameters. The layout extracted from the sliced point cloud is shown in Figure 7.3 that gives an accurate estimation of the geometric parameters as compiled in Table 7.1. In fact, the area computed from the scanned point cloud is an accurate estimate of room because it does not compute it directly by multiplying length and width and takes into account the small variations, protrusions or recesses in the walls.

Table 7.1: The estimated and manually measured physical parameters and percentage variations

Parameters Estimated Value % variation

From Scanned Data Manually Measured

Max. Length (m) _{4.662 4.664 0.043} Max. Width (m) 3.655 3.655 0 Max. Height (m) 3.379 3.381 0.050 Area (m2₎ 16.51 17.04 3.110 Volume (m3₎ 56.029 57.636 2.788

The low variation in the dimensional parameters (length, width and height) is an indicative of capturing accuracy of the scanner. The variation in areas and volume is more as the one computed through manually measurements does not take into account the variation along the span and uses measured length, width and height to estimate the area and volume values. However, the values estimated from the scanned data are more realistic. In case of area computation, the protruded regions due to pillars and wall sections reduce the internal area of the room, which is the exact representation of the corresponding parameter. Moreover, the slice position used to extract the layout for area computation can be altered to better suit the visibility of the dimensional parameters. The volume on the other hand, reduces at the inward protrusions of the wall sections and outwards protrusions of floor section and hence it is actually close to the real value.

7.2.3 Emergency route planning

The extracted contours can also be used to compute the emergency path/route planning. All path planning applications need the section layouts with sufficient information regarding the opening in the layouts for an object/ robot to move around.

As the proposed algorithm extracts the section layouts from data slice, the location of the slice can be adjusted to extract the section layout with desired sectional properties. One slice can generate the section layout with closed boundaries (Figure 7.3(c)) and thereby can be used to compute geometric properties (area). Similarly, another slice from the point cloud can be used to generate the section with recesses (Figure 7.4(d)). The recesses so computed in the extracted layout can be subsequently exploited geometrically and the path or route planning algorithms can be developed for mobile or evacuation applications. Figure 7.4 shows the slice selected from the point cloud of room scan and their corresponding section layout with

door opening. Although this figure shows a section layout of a single room, the methodology is extendable to point cloud captured from multiple locations and registered together, where multiple layouts extracted from the different scan positions can be registered together to combine the overall floor layout. This layout is then used to develop reliable route/path plan.

Figure 7.4: Layout extraction with openings for path/route planning.