## 6 Advancement, Challenges and Future Directions  
Analyzing the strengths and weaknesses of existing research works, some valuable insights are learnt. These are listed as follows:  
* Point cloud-based 3D object detection can be performed in both indoor and outdoor environments. However, LiDAR can generate point clouds at longer range in variable weather conditions, whereas RGB-D or Kinect-based point clouds face limitations in terms of range and weather conditions. For this reason, point cloud-based 3D object detection is performed with the help of LiDAR sensors in autonomous vehicle navigation research. However, RGB-D sensors are less expensive, and the generated point clouds are successfully implemented in close-range research works on precision agriculture or indoor robotic vision;
* Three-dimensional object detection technology is significantly supported by deep learning. Deep learning networks are comprised of multilayer neural networks which can learn the patterns of data. In significant 3D object detection networks, such as PointNet, PointNet++, VoxelNet, CenterNet, etc., deep learning is used to learn object information from points or group of points. Also, in the two-stage networks where initially RGB images are used for region proposals, deep learning is applied for predicting the regions of objects. Future research work may include deep learning for leveraging more opportunities such as transfer learning in 3D object detection-related research;
* Development of end-to-end 3D object detection networks is becoming popular for their ease of application. End-to-end networks directly need to collect raw sensor data and provide 3D bounding-box output predictions. To develop such networks, it is essential to choose the necessary type of sensor (LiDAR, camera or RADAR), pre-process the data, design a neural network to learn the features of the data and train, validate and evaluate the model. Both hardware and software knowledge are required for the developers of end-to-end networks;
* Data collection and annotation for 3D object detection is more complex compared to 2D object detection. Three-dimensional object detection data collection involves fusing the data from different types of sensors, such as LiDAR, monocular or stereo cameras, RADAR, etc. This process requires calibration of different devices and synchronization of the data. The data annotation for 3D object detection needs to describe not only the object location, but also its dimensions in space, position and orientation. The data description involves parameters such as object length, width, height, yaw, pitch, \begin{table}
\begin{tabular}{l l} \hline \hline
**Opportunities** & **Threats** \\ \(\bullet\) & While integrating with other modalities, the vi-sion-based \\ method is proved to be very precise. & \(\bullet\) & Using multi-view 2D images for 3D detection (e.g., in the Structure-from-motion technique) is time-consuming, \\  & method is proved to be very precise. & making it difficult for real-time applications. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{l l} \hline \hline
**Strengths** & **Weaknesses** \\ \(\bullet\) & In combined methods, one device or sensor can over-come \\  & the individual weakness of another. Hence, it provides \\  & better precision than single modality. \\ \hline
**Opportunities** & **Threats** \\ \(\bullet\) & With greater availability of sensors and datasets, combined \\  & methods are gaining popularity among re-searchers \\  & and practitioners. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: SWOT analysis on Multimodal 3D object detection. roll, occlusion amount, etc. More expertise in terms of 3D geometry is necessary to annotate data in the case of 3D object detection;
* The limitation of point cloud-based object detection, especially in outdoor environments, is sparsity. Hence, thin object detection is an open research question in this field.