###### Abstract
The multi-sweep approach takes place in six principle directions. For each voxel, the activated cameras are located on one side of the sweep plane. [PERSON] et al. [PERSON] et al. (2006) explored another approach based on the idea that all potential causes of mismatches, like occlusion, image noise, lack of texture, or highlights, can be uniformly treated as outliers in the matching process. Matching is then seen as a process of robust model fitting to data that contains outliers. In [PERSON] et al. (2006), the model was represented using a cost functional solved via global optimization using Graph-cuts. In [PERSON] et al. (2006) the cost functional took into consideration the non-Lambertian appearance of the scene, and was minimized using the level set method. Volume-based algorithms reconstruct the entire object completely. However, many of them need a good initial estimation of the object to avoid converging into local minima. Their accuracy is usually limited by voxel size. As the size of the voxels becomes smaller, the number of voxels increases cubically, implying ahuge consumption of CPU time and memory. So volume-based algorithms have been demonstrated in the reconstruction of relatively small objects, as shown in Table 1.  
Depth-map fusion algorithms, in principle, are capable of dealing with both small objects and large scenes. Many of them make use of a two-stage approach that estimates a depth map for each image and then merges all the depth maps to extract a final surface [PERSON] et al. (2006); [PERSON] et al. (2006). Early multi-view depth-map fusion algorithms were simple extensions of window-based binocular stereo matchers, which often contained a large amount of noise. Many more advanced depth-map fusion algorithms were subsequently developed utilizing powerful optimization methods like graph cut [PERSON] et al. (2006), expectation maximization [PERSON] et al. (2006), sequential tree-reweighted message passing [PERSON] et al. (2006), etc. These global optimization methods were either used in extraction of each depth map or in the final merging of all the depth maps. [PERSON] et al. [PERSON] et al. (2006) and [PERSON] et al. [PERSON] et al. (2006) went back to find the depth locally. [PERSON] simply discarded the bad matches in texture-weak regions to avoid noise. [PERSON] went much further by introducing a DAISY descriptor [PERSON] et al. (2006). The DAISY descriptor outperforms NCC, since it contains much more information than plain intensity distributions in small windows. For the selection of the best points instantiated from several different image pairs [PERSON] et al. (2006), preference was given to the points obtained by larger focal length, larger baseline and closer camera. To the best of our knowledge, [PERSON] attained the highest reconstruction accuracy [PERSON] et al. (2006); [PERSON] et al. (2006). More importantly, this accuracy was obtained on large-scale, high-resolution data sets with high efficiency, which is of great significance, since millions of high-resolution photos are produced everyday around the world. City-scale [PERSON] et al. (2006) and civil-infrastructure [PERSON] et al. (2006); [PERSON] et al. (2006); [PERSON] et al. (2006) reconstructions have been receiving a lot of attention recently. Feature expansion algorithms provide another efficient way of producing dense 3D clouds. The basic idea of feature expansion or region growing was demonstrated as early as 1989 by [PERSON] et al. [PERSON] et al. (2006) for the processing of satellite photos. They started from a few seed points and performed area-based matching using [PERSON]'s adaptive least-squares correlation algorithm [PERSON] et al. (2006). Following successful matching, the distortion parameters were applied to nearby pixels as initial parameters, and the growing process was repeated for the remaining regions. [PERSON] et al. [PERSON] et al. (2006) provided a surface-growing approach in the form of disk expansion based on the discrepancy induced by the sum of squared differences (SSD).