## 5 Experiments
### Our Approach Compared with Others'  
We compare our experiment results with [PERSON]'s, [PERSON]'s [17] and [PERSON]'s [20], as shown in Figure 7. There are three scenes, and different target objects measured are contained in each scene. Figure 7a,b contains images \(A\) and \(B\), which are captured at different object distance by a common camera. The spatial relationships of the objects measured are shown in Figure 7c. The images are all taken from a top down view. They are convenient for us to estimate roughly whether a target object is near or far from the camera. Figure 7d-g shows the depth images as measured by [PERSON]'s method [17], [PERSON] device, [PERSON]'s method [20] and our method, respectively. In Figure 7d, [PERSON] gets dense depth maps, but they are relative depth information instead of absolute depth. Only depth of partial scenes are measured in Figure 7e,g, but they are absolute depth; namely they are object's distance values, which are represented by different pixel values. Both the gray pixels in Figure 7e and the white pixels in Figure 7g indicate that the depth information of the place could not be measured. In Figure 7e-g, the different color or gray pixels mean different depth values. There is some obviously incorrect depth information in Figure 7d. For example, in every image, the gray levels of the two objects which are indicated by red arrows are similar. It means that their depths as measured by [PERSON]'s method are similar, but, in fact, the disparity of the depth values is very big. The reason for this error is that [PERSON]'s method can only measure the defocus degree, and can't judge the focal plane in front of or behind the imaging plane. Furthermore, the depth of the object which is indicated by blue arrows should be the same as its surroundings. But the results of the measurement are not consistent with the ground truth, because the texture of the image affects the construction accuracy of dense depth maps, which are obtained by applying matting Laplacian to perform sparse depth map interpolation [17]. We can also see that there are several obvious errors of depth measurement in Figure 7e, as the white arrows indicate. In fact, the depth values of the two places are very different, but the depth values measured by Bumblebee device are similar. So the colors of the two places are very similar in the images. In addition, the outlines of the objects in the depth images are different from the real objects. This was caused by error of measurement. Figure 7f is the result of depth image by [PERSON]'s method. The method is faster than the state-of-the-art method, and it can achieve quasi real-time performance. But the accuracy of depth information is not good. It is difficult for us to find the outlines of the objects from the depth image, especially the outlines of the small objects. In addition, the method needs a dataset to train and learn. Figure 7g is result of depth image by our method. We can see that we can obtain a high accuracy in absolute depth of the target objects, and the outlines of the objects are clear and accurate in the depth images by our method. In Figure 7g, the depth of the watering pot head is shown, but the depth of the watering pot body cannot be measured, because the watering pot was divided into two parts--the watering pot head and the watering pot body after image segmentation--and not enough SIFT feature points could be detected on the watering pot body, due to the color of the body being very close to that of the background. Usually, if no less than 4 SIFT points are detected on an object, and then we can use them to compute the depth of objects. Therefore, our method works well in most cases. Figure 7: Cont. Experiment images of the other ten scenes are showed in Figure 8. From left to right, each column is image \(A\), image \(B\), results of our method, results of the method of [PERSON], and the rough spatial relationship of the measured objects, respectively. Figure 8: \(Cont\). Figure 7: Comparison of depth estimation results of different approaches.