# Fast 3D Transmission Tower Detection Based on Virtual Views
## 2 Related Works
Different object projections are prone to overlapping, making detection tasks more difficult. In 2018, [PERSON] et al. [15] proposed BirdNet, a framework that initially transforms point clouds into a bird's-eye view (BEV) map, incorporating height, intensity, and normalized density as its three key feature layers. Subsequently, this BEV map serves as input for a faster R-CNN network, which is utilized for 2D object detection. Additionally, the method integrates ground plane estimation to predict the orientation of targets. BirdNet is structurally simple, ensuring real-time detection, but its detection accuracy is not high. [PERSON] et al. [16] proposed a single-stage dense object detection framework named PIXOR, which projects point cloud data to obtain a BEV and apply a lightweight CNN for feature extraction, performing pixel-level dense prediction to improve detection precision. Subsequently, [PERSON] et al. [17] optimized PIXOR and proposed HDNet, which introduces map and road semantic information to assist in object detection. Compared to PIXOR, the network structure replaces original deconvolutional layers with more efficient bilinear interpolation layers, enhancing performance. In 2021, [PERSON] et al. [18] proposed a novel offboard 3D object detection pipeline using point cloud sequence data by making use of the temporal points through both multi-frame object detection and novel object-centric refinement models. In 2024, [PERSON] et al. [19] proposed a new 3D occupancy voxel mapping method for object detection and a cluster-based multiple-hypothesis tracker to compensate for the uncertainty of the sensory data, thereby providing robustness to different environments and appearance changes of objects. BEVs significantly reduce object overlap compared to front or side views, minimizing the impact of target occlusion. Furthermore, they offer a unified and comprehensive global scene representation, allowing for the direct and clear expression of object size and orientation. However, its efficacy significantly diminishes when detecting small objects, such as the components of TTs within the context of automated inspection scenarios. [PERSON] et al. [20] proposed LaserNet, a method that projects point clouds to generate six panoramic views, employs convolutional neural networks for feature extraction and, subsequently, integrates the extracted features from these six perspectives for fine object detection. [PERSON] et al. [21] proposed RangeDet, which projects point cloud data to obtain a range view and apply a Meta-convolutional kernel for feature extraction. Compared to BEVs, range views are more compact and have no quantization errors. RangeDet addresses the \"pain point\" of poor performance in range view-based object detection methods and even surpasses BEVs in detecting smaller or more distant objects. Some researchers have also leveraged multiple views to achieve more accurate 3D object detection. In 2024, [PERSON] et al. [22] proposed a multi-branch, two-stage 3D object detection framework that utilizes a semantic-aware multi-branch sampling module and multi-view consistency constraints to enhance detection performance. In the field of object detection for point cloud data, there is often a trade-off between speed and accuracy, meaning that improving detection accuracy often requires more computational resources and time, and vice versa. Point-based methods have complete information but are difficult to speed up due to the overwhelming amount of information. Voxel-based methods also face the same issue when increasing the voxel sampling resolution to pursue high detection precision. Projection-based methods can make information representation more compact, thereby reducing computational complexity and optimizing detection efficiency. However, BEVs, side views, or range views can cause an inevitable loss of information when converting from 3D to 2D. Therefore, it is important to select the appropriate method based on the specific scenario. In the context of related works, our contributions are as follows:  
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