# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 3 Technology Path of Image-Based 3D Reconstruction
### Point Cloud Semantic Recognition  
The point cloud generated by laser scanning is a group of indistinguishable points that only contains the 3D coordinate of points. The points representing various components are glued together. However, the point clouds generated from RGB or RGB-D image can contain color, texture, and other information. They can be delivered to the point cloud  
Figure 5: Projections of input model lines with divergent camera poses by rotation (**a**–**c**) and translation (**d**–**f**) [4].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segmentation Methodologies** & **Advantages** & **Disadvantages** & **Ref** \\ \hline Clustering-based & Easy to understand and implement & Accuracy problem: sensitive to the noise in data and is influenced by the definition of neighbor & [47; 48] \\ Edge-based & Fast segmentation & Accuracy problem: sensitive to noise and uneven density of point clouds & [49; 50] \\ Region-based & More accurate to noise & Over or under segmentation and accuracy of determining boundaries & [51] \\ Graph-based & Better on complex point cloud data & Cannot process in real time, and training or other system is required to assist process & [50; 52] \\ Model fitting-based & & & \\ Hough transform & Fast and robust with outliers, can process a large amount of data & Slower and more sensitive to segmentation parameters & [53] \\ Hybrid & Take advantage of multiple approaches more accurate & Accuracy when processing different point cloud sources & [54] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of data segmentation methodologies for point cloud data.  
through the mapping relationship between the point cloud and the image to help identify which primitive a point belongs to.  
Many scholars mark semantic information for point clouds in various ways, which is called semantic recognition/labeling. The key to semantic recognition is to establish semantic mapping. In general, practice, the point cloud is segmented into small homogeneous 3D patches, and then the features (including color, position, height, compactness, linearity, planarity, angle with the ground, etc.) of each patch are extracted to classify these patches to form semantic point cloud. [PERSON] et al. [56] proposed a multi-view frame fusion technique to enhance the semantic labeling results with 3D entangled forests and built semantic maps on RGB-D point cloud. The point cloud was over-segmented into homogeneous 3D patches and a feature vector of length 18 was calculated for each patch. Five binary tests defining the entangled features were used to describe complex geometrical relationship between segments in a neighborhood. [PERSON] et al. [57] presented a purely semantic mapping framework which operates solely with omnidirectional images. The free space was found from the omni-image with a binary floor/obstacle classifier. In addition, a place category classifier was used to label the navigation relevant categories: room, corridor, doorway, and open room. [PERSON] et al. [58] divided the semantic modeling process into five semantic levels, including (1) automatic data acquisition of the building's as-is state, (2) simple geometric building model, (3) recognition and labeling of primary structural elements (SEs) of the building, (4) recognition of openings within SEs of the building, and (5) recognition of small building service components on SEs, as shown in Figure 6. The integrated system they proposed can automatically reconstruct large scenes at a high level of detail and provide detailed as-is semantic models of building. [PERSON] [59] presented an image-based material classification method for semantically rich as-built 3D modeling, and a CML was formulated to train and test the proposed method, as show in Figure 7. Although their method was only used on images, it is feasible to graft this technology into point cloud semantic recognition.  
Figure 6: Levels of semantic 3D modeling [58].