## 4 Discussion  
According to the experimental results, we can outline several interesting research findings for 3D computer vision:  
* To the best of our best knowledge, there are very few researchers working on the problem of pose estimation using a single RGB image. Conventional pose estimation algorithms use a sequence of RGB images to compute the depth map and the external camera parameters. However, this highly increases the complexity of the resulting pose estimation. On the contrary, in this work, the input to the model-based pose estimation algorithm is only a single image. This facilitates the real-time reconstruction of 3D models.
* The tedious human annotation effort required to prepare a large amount of training data for improving the recognition accuracy of the DVCNN is avoided by representing scene point clouds as a collection of super-points using the MPPA algorithm. Although the number of training RGB-D image frames for 3D scene modeling is often small, the average recognition rate for viewpoint classification using the DVCNN achieves 98.34% according to our experimental results. Moreover,  
Figure 11: (**a**) An example of the 2D trajectory and error of the proposed method using the test dataset ‘freiburg1_desk’. (**a1,a2**) are the 2D trajectory error without and with the rumovement of the outliers of corresponding pairs, respectively, when using Algorithm 3 to estimate the poses. (**a3**) is the difference comparison of the bounding box in (**a1,a2**). (**b**) The reconstructed 3D scene using the proposed template-to-frame registration technique.  
the learning curves of testing and learning for the DVCNN closely coincide with each other. This implies the overfitting problem of conventional deep neural networks is solved even when the training data is automatically labeled.
* The performance of the proposed pose estimation is obviously dependent on the number of templates used to model a 3D scene. As shown in Table 2, the usage of fewer templates for 3D scene modeling leads to more significant errors in registration between the current point cloud, generated by the input RGB image, and the model point cloud. In the application of abnormal event detection, the registration error introduced by the model-based pose estimation algorithm should be minimized to a very small degree in order to reduce the number of false positives in the construction of an event alarm system. Thus, we suggest using more templates in 3D model reconstruction even though it increases the computational complexity.
* The usage of deep features obtained by the deep neural networks shown in Figure 4 is not suggested when the training datasets are not large enough. The usage of features of super-points and super-pixels offers good discrimination power in the learning of the DVCNN according to our experimental results. The proposed MPAA is actually much like the conventional PCA dimensionality reduction technique. This implies that the claim that deep learning outperforms traditional learning schemes in all aspects of applications is a pitfall.  
The proposed 3D scene modeling can model the background information fast and accurately. Thus, a real-time vision-based scene surveillance system can be easily constructed using a simple background subtraction algorithm. Moreover, the problem of unstable background degrading the performance of the simple background subtraction scheme for visual inspection is avoided since our pose estimation algorithm eliminates the slight changes in the model under surveillance along the timeline using the model updating scheme.