Semantic segmentation, a task vital in the creation of 3D point cloud models for buildings, is aimed at assigning meaning to individual points. However, due to the vast volume of unstructured point cloud data, precise semantic segmentation remains challenging. Significant progress has been observed in recent years with the application of deep learning techniques to point cloud segmentation, and the effectiveness of Dynamic Graph Convolutional Neural Network (DGCNN) and K-Nearest Neighbors (K-NN) in handling point cloud data has been recognized.
In this study, Dynamic Graph Convolutional Neural Network (DGCNN) was utilized for semantic segmentation on a building's point cloud scene. We adopted K-Nearest Neighbors (K-NN) as a crucial component of our methodology to optimize the segmentation process. By varying 'k' values in K-NN and exploring different block sizes, we aimed to obtain various segmentation results for comparison. When a block size of 1 meter was employed and 'k' was set to 20, an overall accuracy of 90.32%, mean accuracy of 87.64%, and IoU of 80.71% were achieved. However, the most favorable segmentation outcomes were observed when the block size remained 1 meter, and 'k' was set to 30, resulting in an overall accuracy of 93.86%, mean accuracy of 90.68%, and IoU of 84.97%.
These experiments underscore the significance of parameter selection in optimizing the performance of DGCNN for point cloud segmentation. The findings reveal that adjustments to 'k' values and block sizes can significantly influence segmentation accuracy and quality, emphasizing the importance of parameter optimization in the context of semantic segmentation for building point clouds using deep learning techniques. The utilization of K-NN played a crucial role in achieving these improvements by allowing us to adapt to the inherent variability in point cloud data.

The 3D point cloud representation of building holds significant importance in developing a comprehensive smart city model. However, due to the substantial volume of point cloud data and its inherently unstructured nature, accomplishing semantic segmentation of the point cloud poses a formidable challenge. Recent years have witnessed substantial advancements in employing deep learning techniques for point cloud segmentation. Both PointNet and PointNet++ have demonstrated their prowess in managing point cloud data. In the context of this study, we harness the potential of DGCNN for segmenting building point clouds. DGCNN plays a pivotal role in partitioning the input point cloud, thereby influencing the sensor domain's network range. Consequently, this research delves into the role of parameter k in K-NN (K-Nearest Neighbors) and its implications. The findings illustrate that augmenting the 'k' value contributes to enhancing the overall precision of DGCNN. Nonetheless, complete precision in segmenting every module cannot be assured. The research objectives in the study is building façade, and the targets are windows, walls, balconies and doors. In this study, the key parameters are the values of 'k' and the 'block size.' Experiments were conducted by varying the values of these two parameters to obtain different segmentation results of building facade point clouds. Notably, setting k to 20, block size to 1m achieves an overall accuracy rate of 89.75%, a mean accuracy rate of 85.03%, an IoU of 76.24%, while elevating k to 30 and block size to 1m results in a heightened accuracy rate of 95.74%, a mean accuracy rate of 89.37%, an IoU of 81.52%.