## 3 Proposed Work
### State Space Model
The input and ground regions of the PC are then segmented and avoided using this object. The distance threshold is set at 0.5. The ground plans are then labeled. The output is the number of clusters and their labels, which are obtained using M \(\times\) N integer matrices. Every point within a cluster is assigned a label that falls within a range of 1 to the total number of clusters. Points that are deemed unacceptable, such as those labeled as \"Not a Number\" (NaN) or \"inf\", are allocated a label of \"0\" based on the IEEE754 2008 standard for maintaining floating point values. The count of valid clusters can be represented by looking at the total number of positive labels assigned to the points. Finally, the PC data are segmented based on color and prepared for use. Image labelling that focuses on recognizing and labelling certain aspects in a picture is known as image labelling. Pre-learning applications for categorizing three-dimensional point clouds are becoming more popular as a result of it. The choice of either a single scale or many scales can be the primary consideration when choosing a neighborhood. By postponing the decision on the appropriateness of a neighborhood for the predictor, one can assess its characteristics across various scales. However, numerous additional factors must also be considered, such as the architecture of the scale space, the number of scales involved, and the technique used to calculate the distance between scales. These decisions, which are often based on heuristics or empirical selection [37], are unique to each dataset. In the context of the preceding to characterize the local 3D design. Finding the 3D points in the vicinity of the specified 3D point \(X=(x,y,z)^{T}\) belonging to R is the primary challenge. This necessitates (i) a suitable definition of the neighbourhood, (ii) an efficient method for recovering nearby 3D locations, and (iii) a sufficient parameterization based on the neighbourhood's size. We give these three issues careful consideration. The properties of the relevant PC data will undoubtedly affect the choice of the appropriate neighborhood definition and scale parameter. In comparison to the cylindrical as well as spherical definitions of neighborhoods \(N_{s}\) and \(N_{C}\), which, as discussed in Section 2, require direct empirical or heuristic knowledge of the scene to determine an appropriate radius, \(N_{k}\), the neighbourhood description, allows for greater flexibility in the face of changing point densities. We chose the \(N_{k}\) definition, which is dependent on the KNN of a provided 3D point \(X\), as it provides a flexible framework that is not limited to a specific dataset. We can generate a spherical neighborhood with a radius that can vary by selecting the nearest neighbors dependent on their 3D distances [38]. When choosing a suitable number of neighbors \(k\) to consider, the simplest method would be to choose a fixed value of \(k\) for each point in the PC [39]. In this sense, the existing heuristic or empirical knowledge still plays a role in the selection of \(k\). However, it is intuitive that a more adaptive and dynamic approach, where the variables \(k\) (also referred to as the scale) can differ in the dataset, would be preferable. This is because \(k\) is clearly influenced by the 3D structures and local point density in the dataset. Therefore, it would be ideal to use a standardized approach to determine the optimal local neighborhoods. The Pre-learning algorithm is discussed in Algorithm 3. ``` Input: \(X\), \(\alpha\), \(\beta\), \(\gamma\), \(\gamma\), \(\gamma^{\```
Input: LiDAR PC raw data Output: Labelled Point-wise segmented 2D
1. Capture the surroundings
2. Alter the PCAP file into PC object
3. Eliminate ground segment from PC data
4. Eliminate the points that are not valid from PC
5. Divide image frames and follow steps for frames numbered from 1 to n
1. Initialize the point weights
2. Create ground truth vector as input for the building
3. Examine the point to find the weights that are similar to the input vector. 4.