# Fast 3D Transmission Tower Detection Based on Virtual Views
## 2 Related Works  
Three-dimensional (3D) point cloud data encompass rich spatial information, providing precise spatial positioning details of scenes, and playing a significant role in the localization and prediction of targets. Moreover, 3D object detection can be broadly categorized into the following types:  
* Projection-based methods: These methods process 3D point cloud data by projecting the data onto two-dimensional (2D) views (such as front view, top view, side view, etc.). This category leverages mature 2D image processing techniques, such as convolutional neural networks (CNNs), for object detection. The advantage of this approach is the ability to utilize powerful tools and algorithms from the image domain, but it may lose some depth information and may not be efficient in handling occlusion issues. * Point-based methods: These methods operate directly on the raw point cloud data without voxelization or other forms of transformation, directly utilizing the geometric distribution and density information of the point cloud. Methods like PointNet [8] and PointRCNN [9] preserve the complete 3D information of the point cloud but have higher computational complexity. * Voxel-based methods: These methods divide the 3D space into a regular grid and quantize the point cloud into 3D cells (voxels), then apply 3D CNNs for further feature extraction and object detection. Methods like VoxelNet [10] and PointPillars [11] can reduce the computational complexity to a certain extent while maintaining the spatial structure information but may introduce information loss, especially at a low sampling resolution. * Point-voxel-mixed methods: These methods combine the advantages of the two aforementioned methods, utilizing both the precise geometric information of the point cloud and the computational efficiency afforded by voxelization. They aim to optimize the use of computational resources while maintaining accuracy, for example, by usingvoxel representation in dense areas of the point cloud and directly processing point cloud points in sparse areas. Each category has its specific application scenarios and advantages, and the most suitable method should be chosen based on specific requirements, such as accuracy requirements, real-time constraints, hardware resource limitations, etc. Point-based methods, such as PointNet [8], PointNet++ [12], PointRCNN [9], and FARP-Net [13], do not require data transformation, thus avoiding the loss of effective information. However, point cloud data are voluminous, typically consisting of tens of millions of points with 3D coordinates and other attribute information, such as colors, returns, etc. The uneven distribution in 3D space makes point-based methods computationally expensive and slow. Voxel-based methods require the transformation of point cloud data into regular voxels, using voxels as input for 3D object detection. These methods extend traditional 2D object detection networks to 3D domains and achieve higher efficiency than point-based methods. However, converting non-uniform point clouds into regular voxels may result in a lack of effective information, and due to the sparsity of point clouds, most of the converted voxels are empty, which indicates a significant amount of computational effort and memory will be wasted. To achieve a more efficient representation of point cloud data for 3D object detection, many researchers have explored methods to transform point cloud data onto a 2D plane, such as generating bird's-eye views or side views. This allows the use of existing mature 2D object detection networks for fast target recognition. In 2016, [PERSON] et al. [14] adopted an innovative strategy, VeloFCN, which projects point clouds onto a front view and converts them into 2D images, then uses a fully connected network to simultaneously complete object detection and location regression tasks. However, in the front view representation, the sizes of object projections in the image are inversely proportional to their depth.