## 3 Branches of 3D Object Detection Techniques  
Three-dimensional object detection is gaining popularity in various domains. Recent research works on 3D object detection in different application sectors are shown in Figure 3.  
The application of 3D object detection is expanding day by day. Figure 3 depicts the commonly practiced sectors of 3D object detection. Among all the four sectors, most research work is conducted in the autonomous vehicle navigation domain. The benchmark datasets are most versatile in this sector as well. Research works in these domains are tabularized in Tables 3 and 4. Apart from the above-discussed applications, 3D object detection is also performed in some recent medical research, such as augmented assistance in surgery or diagnosis. The input data format and acquisition process of 3D object detection in the medical field is different than other sectors. Computed Tomography (CT), Diffusion tensor imaging (DTI), magnetic resonance imaging (MRI), Functional magnetic resonance imaging and Ultrasound are three-dimensional. Three-dimensional convolutional networks can be applied to these images for 3D detection of organs for better disease detection. Three-dimensional CNN is successfully applied for disease severity prediction and classification [99; 100; 101]. However, 3D datasets are very few in number in the medical field. Patient confidentiality may be a reason behind it. Image augmentation is sometimes applied to increase the dataset size [102; 103]. In Table 3, we have summarized singular modality 3D object detection methods practiced since 2017. Multimodal techniques involve multiple sensors for 3D object detection. In Table 4, recent multimodal techniques are mentioned. This table indicates that multimodal techniques usually involve LiDAR or RGB images accompanied by some other modality. Tables 3 and 4 exhibit singular and multimodal techniques of 3D object detection in different fields. Observing the trends, the following insights are found. * Autonomous Vehicle navigation: LiDAR is a very popular modality in this field. It can be used both in singular and multimodal methods. LiDAR exhibits long-range  
Figure 3: Applications of 3D object detection in different sectors. LASER scanning, making it capable of designing a standalone end-to-end 3D object detection system. RGB-D sensors have low range (usually lower than 10 m). Due to this constraint, autonomous vehicle navigation-related research works are not found to use this modality. Among the vision-based technologies, a monocular RGB camera was used as a singular modality by few researchers, but it could not exhibit as much accuracy as the LiDAR sensor. Moreover, detectability range was lower than LiDAR. Some researchers have mentioned stereo cameras as a modality with high potential in autonomous vehicle navigation. Even the automotive company Tesla is focusing on stereoscopic vision rather than LiDAR, considering it more nature-inspired, economical, and similar to human vision. However, stereoscopic camera ranges are far less than that of LiDAR. RADAR is not found to be used as a singular modality system for 3D object detection since, it cannot perceive color information of the objects. But in some research works, RADAR was implemented along with other modalities. * Robot Vision: RGB-D sensor is the most popular sensor, serving both in singular and multimodal techniques of robot vision. Most of these research works are conducted in indoor environments. For this reason, the long-range detection requirement is absent in this field. This makes RGB-D a wonderful option for perceiving both color and depth information of objects in indoor environments. RGB-D cameras are constructed with an RGB camera (for color perception) and infrared sensors (for depth perception). * Precision Agriculture: In agriculture, LiDAR is found to be used for long-range 3D detection. Specially, precision agriculture involving UAVs from high altitude is benefitted by LiDAR.