## 3 Results and Discussion  
### Typical Results  
We have tried our algorithm with good results for the reconstruction of various kinds of scenes, featuring small and big sizes, simple and complicate structures, strong and weak textures, changing illuminations, moving obstacles, occlusion, etc. The data sets were from Technical University of Denmark (DTU) benchmarks [1; 2], Middlebury benchmarks [3], or captured by ourselves. Figure 5 presents some typical images from the data sets used in the experiments. The parameters for the data sets are listed in Table 2. Figure 6 gives the corresponding reconstructed color 3D point clouds observed from different viewpoints. In our experiments the reconstruction parameters were set as \(N_{1}=150\), \(th_{\sigma 2}=36\), \(th_{d}=8s\), \(th_{c}=0.3\), \(th_{dp}=0.342\), \(a_{1}=s\), \(a_{2}\)\(=1.5s\), \(a_{3}=2s\). These parameters, which were mostly for thresholds, were either fixed or changed with step \(s\) in all our experiments and worked well. The reason might be that the framework of the proposed algorithm made the reconstruction of each point relatively independent. An outlier, even if it was created and escaped exclusion by one filter in a particular situation, might hardly escape notice by another filter and subsequent initial value modification and re-optimization. In other words, it could hardly spread to other regions. Our experiments were performed on a 2.4 GHz Pentium computer. The proposed algorithm was coded in Matlab without careful optimization of the loops, the reading and writing of data files, etc. So, it took a longer time than some other state-of-the-art algorithms do, which have usually been implemented in the C language. For example, the _volleyball_ and _house model_ took about 4 h and 6 h, respectively. However, significant acceleration on speed is expected when the algorithm is carefully optimized and transformed into C language and further aided by parallel computation, which is our next goal. Figure 4: Two kinds of depth discontinuities: (**a**) Depth discontinuity caused by occlusion; (**b**) Depth discontinuity caused by an outlier. Figure 5: Typical images for data sets used in experiments. Data sets in the first row from left to right are _dino_ (one image), _temple_ (one image), _volleyball_ (two images) and _house model_ (two images). The data set in the second row is _science hall_. Data sets in the third row from left to right are _lion_ and _quadrupol_, each with three images. Figure 6: Some reconstructed 3D colored point clouds with different viewpoints. In the first row from left to right: _dino_ (two views), _temple_ (two views), and _volleyball_ (one view). In the second row from left to right are _house model_ and _science hall_. In the third row from left to right are _lion_ and _quadrupol_ each with two views. As shown in Figure 5, the _dino_ and _volleyball_ were smooth with a weak texture. Yet they were reconstructed successfully as shown in Figure 6. The _temple_ and _house model_ bore many regions with high curvature, e.g., the pillar in the red rectangle and the antenna (about one millimeter [1; 2]) in a green rectangle in Figure 5. Yet all these highly-curved surfaces and thin structures were reconstructed clearly, as can be seen in Figure 6. The _lion_ and _quadrupol_ in Figure 6 gave examples of the reconstruction of scenes with moving pedestrians and different illumination. For example, the _lion_ was shot in two days with different environmental light, as can be seen in the third row of Figure 5. _Science hall_ in Figure 6 provided an example of the reconstruction of big and complicated scenes. There were moving pedestrians and moving cars, fixed cars, working LED screen and so on, as shown in the second row of Figure 5. Besides, all the images have a looking-up view direction since they were shot from the ground.