## 3 Results  
The system is implemented on a PC with Intel Core i7 3.4 GHz CPU and NVIDIA GeForce 9800 GT. The RGB-D benchmark provided by the Technical University of Munich [44] is used to verify the effectiveness of our approach in terms of the accuracy of the viewpoint classification and pose estimation. In this benchmark, several datasets [51] including aligned color and depth sequences captured with a single RGB-D moving camera are provided. The accurate ground-truth of each frame's camera pose is also provided, which is measured by an external motion capture system. The image frames of each dataset are separated into two disjoint sets: a training dataset and a test dataset. Figure 7 shows an example of an RGB frame in each dataset. In the application of object modeling and inspection, the CNN object detector is applied to locate the target objects in the RGB images. To train the CNN object detector, the set of bounding boxes that locate the target objects in the training RGB image frames is obtained by human annotation. The accuracy of the resulting CNN object detection obviously affects the accuracy of the modeled 3D object. To verify the effectiveness of the usage of CNN object detector in our application, both the model-specific and global CNN object detectors are trained using a single dataset and all datasets, respectively. Figure 8 shows the performance comparison between these two modes of CNN object detectors. One of the advantages of the usage of the global CNN object detector is that we just need to train a single CNN object detector which detects all objects in different models. However, the performance of the global CNN object detector is not good enough to provide accurate object modeling results. On the contrary, the model-specific object detector achieves average accuracy of up to 98.62% and thus guarantees the accuracy of the resulting 3D object model. Thus, we train multiple model-specific CNN object detectors which are further integrated to achieve the goal of detecting all objects under surveillance. The model-specific CNN object detector that gives the largest probability on the detected object is used to annotate the object in a test RGB image. This preserves the high accuracy advantage of the model-specific CNN object detector, which can be verified by the results shown in Table 1.  
Figure 8: Performance comparison between the model-specific and global CNN object detectors using example datasets: from left to right are the results of ‘freiburg1_plant’, ‘freiburg2_caninet’, and ‘freiburg3_teddy’. The left column and the right column in each cell are the results of the model-specific and the global CNN object detectors, respectively. Figure 7: Example image frames of the test datasets. The second experiments demonstrate the effectiveness of the proposed viewpoint classification. Figure 9 shows the view classification results using the dataset 'freiburg3_teddy'. The RGB template of a viewpoint and those of test images that are classified to the viewpoint class are very similar in terms of subjective evaluation. Given the training dataset 'freiburg3_teddy', with the clustering results as the viewpoint labels, the recognition accuracy of our viewpoint classifier achieves 98.24% without suffering from the overfitting problem. To compare the performance of our approach with other state-of-the-art methods in pose estimation [39; 40; 52; 53], five typical datasets are selected as test samples. In [39; 52], the compared approaches are model-based methods which are similar to our approach. In [40; 53], the authors proposed RGB-D SLAM systems. Some postprocessing methods, such as global optimization are used to modify the global camera trajectory in these methods. Table 2 summarizes the experimental results. The first column is the dataset name. The remaining columns are the results for the root mean square error (RMSE) of the relative errors. The relative error is the translational drift in m/s between the estimated pose and the ground-truth.