## 3 Analysis of Aging Building Remodeling and Object Recognition Technology
### Analysis of Deep Learning-Based Point Cloud Object Recognition Technology  
Point clouds and triangle meshes serve as robust representations, preserving intricate surface details essential for conveying comprehensive geometric information related to the structure of buildings and building components, as well as their interconnections [8]. The advent of deep learning has notably enhanced object recognition within this context. Deep learning leverages its capability to acquire prior knowledge of object features by performing extensive feature calculations on substantial datasets in advance [9]. The method for searching for objects within point cloud data is not significantly different from searching for objects in traditional images using networks [10; 11]. In conventional images, objects are labeled with Bounding Boxes in x and y coordinates, and networks,  
Figure 3: Overview of the proposed 3D object detector from the Bird’s Eye View (BEV) of the LIDAR Point Cloud. primarily based on Fully Convolutional Neural Networks (FCNN), are used to locate objects in new images. The method for searching for objects within point cloud data is fundamentally similar [12]. Recent state-of-the-art methods for object recognition include point-cloud object recognition based on histograms of dual deviation angle features (HDDAF) [13] to detect unknown objects in point clouds by matching histograms of unknown objects with a model library. [PERSON] et al. [14]. Developed an effective and efficient 3D object detector using a voxel-point geometry abstraction scheme for acceleration and the generation of proposals. The previously mentioned network, FCNN, is one of the key networks that has rapidly advanced the development of deep learning technology. It is effectively used for object segmentation in high-resolution images. FCNN is an extension of traditional convolutional neural networks designed for image segmentation tasks [15]. This network has significant applications in semantic segmentation, object detection, which accurately predicts object positions and boundaries, and natural language processing, spanning fields such as roads, autonomous driving, medical image analysis, and more. Semantic segmentation in point clouds is also quite similar to semantic segmentation in 2D images [16]. In traditional 2D images, labeling is done pixel by pixel for training, while in 3D, points are labeled similarly to pixels for training. Figure 4 illustrates the structure of FCNN. The foundational structure of FCNN involves augmenting the VGG 16-layer convolution network with a multilayer deconvolution network. This addition aims to produce a precise segmentation map for a given input proposal. Utilizing the feature representation derived from the convolution network, employ a sequence of unspooling, deconvolution, and rectification operations to construct a dense pixel-wise class prediction map [17]. Among the deep learning models used for processing point clouds, PointNet is currently one of the most prominent models in terms of performance. The generic object recognition algorithm is: Achieve high quality and accuracy while maintaining high efficiency [18]. PointNet efficiently addresses the structural disorder inherent in point cloud data by realizing effective end-to-end feature learning. PointNet avoids data transformation and handles displacement invariance of input points [19]. Additionally, PointNet has been successful in tasks such as segmentation and object classification, outperforming previous methods such as PointNetX. PointNet is highly effective in performing tasks such as 3D object classification, segmentation, and recognition by extending traditional 2D image processing techniques into the 3D space [20]. PointNet takes 3D data represented as points or point clouds as input. This model learns the features of each point and integrates them to recognize or segment objects. One notable feature of PointNet is its ability to generate consistent results regardless of the input order.