## 5 Experiments
### Our Approach Compared with Others'
(**a**)This row contains the original images \(A\) taken by a common camera; (**b**) this row contains the original images \(B\) taken by the same camera, only the object distance is different; (**c**) this row of images, which are taken from a top down view, show the space relationship of measured objects. They are convenient for us to judge if an object is near or far from the camera, roughly; (**d**) this row contains the depth images as measured by the Bumblebee device; (**e**) this row contains the depth maps that measured by the method of [PERSON] [17]; (**f**) this row contains the depth maps that measured by the method of [PERSON] [20]. The source code is available on GitHub ([[https://github.com/king9014/rf-depth](https://github.com/king9014/rf-depth)]([https://github.com/king9014/rf-depth](https://github.com/king9014/rf-depth))); (**g**) this row contains the depth images that measured by our method. In order to determine the degree of accuracy of result of our method, we took 191 group images for experiments. There are 382 images, for a group includes 2 images. And there are 3 target objects measured in a group, so 573 depths of objects should be measured. In our implementation, we used the same parameters for the whole experiment, i.e., \(T_{p}\) = 0.6, \(T_{s}\) = 0.6, \(T_{\theta}\) = 3\({}^{\circ}\). We compared the result of our method with the result of [PERSON] et al. 's method [19], as shown in Table 3.  
In [PERSON] et al. 's method, SIFT is employed to obtain the depth of objects, too. Firstly, a database needs to be built, and the measured objects must be registered in the database, or their depth cannot be measured. The database comprises object distance, a lot of images and their SIFT. The images of each measured object are taken from different object distances and different angles of view. A uniform and simple background is required in the images. The object distance is obtained by a laser device. Then, the data in the database is used for the training of their algorithm. For an image, the depth of the objects in the image can be computed only when the objects have been stored in the database. Above all, SIFT feature points should be detected. Then, the center of mass of feature points set and the average distance \(d_{m}\) between it and every feature point should be calculated. Lastly, the depth of objects can be computed by using \(d_{m}\) and some data in the database. Figure 8: The experiment images of the ten scenes. The column (**a**) contains the original images \(A\). The column (**b**) contains the original images \(B\). The column (**c**) contains the depth images that were measured by our method. The column (**d**) contains the depth images that measured by the method of [PERSON] [17]. The column (**e**) images, which are taken from a top down view, show the spatial relationship of the measured objects. They are convenient for us to judge if an object is near or far from the camera, roughly. \begin{table}
\begin{tabular}{c c c} \hline
**Item Compared** & **Our Method** & **[PERSON] et al.’s Method** \\ \hline Device required & camera & camera and laser depth measurement device \\ Number of images required & 2 & \(\geq\)5 images for every measured object \\ Is a sample database required? & NO & YES \\ Can the depth of object which is not & YES & NO \\ registered in the database be measured? & 5.14\% & 9.89\% \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison between our method with [PERSON] et al.’s method [19]. Table 3 shows advantages of our method include fewer devices, easy operation, no sample database, and less measurement error. Figure 9 is the comparison of depth of the objects measured by our method with the ground truth. The horizontal axis represents the serial number of the measured objects. The vertical axis represents the depth of the measured objects. The red curve is the depth measured by our method, and the blue one is the ground truth, and. The maximum error is 16.1%, and the minimum error is 0.01%, and the average error is 5.14%.