###### Abstract
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Authors-Year** & **Technical Features and Sample Result** \\ \hline \multirow{7}{*}{Volume based algorithms} & [PERSON] et al., 1999 [5] & Voxel coloring, Camera positions were constrained to handle occlusion problem. \\  & & \(\lambda\) dinosaur try was reconstructed with 71,841 grids out of 166 \(\times\) 159 \(\times\) 233 initial grids. \\ \cline{2-3}  & [PERSON] et al., 2000 [6] & Space carving, Camera positions are not constrained. Occlusion problem was solved by multi-pass plane sweep. A hand with 112k voxels was reconstructed. \\ \cline{2-3}  & [PERSON] et al., 2005 [7] & Shape evolution, Discrepancy function was minimized by level set method. \\ \cline{2-3}  & [PERSON] et al., 2007 [8] & Volume graph-cut. Global cost functional is minimized to obtain shape and avoid occlusion. Temple data set was reconstructed. \\ \hline \multirow{7}{*}{Depth-map fusion algorithms} & [PERSON] et al., 2002 [9] & Energy minimization by graph cut. An energy formulation was given to model photo-consistency, smoothness and visibility at the same time. Four 384 \(\times\) 288 depth maps for Tsukuba data were computed. \\ \cline{2-3}  & [PERSON] et al., 2006 [10] & Window-based matching. A depth map was reconstructed for each image and only those points with high confidence based on Normalized Cross Correlation (NCC) were selected. The nkallah dataset was reconstructed. \\ \cline{2-3}  & [PERSON] et al., 2008 [11] & Markov Random Field (MBF) optimization. A reliable depth was selected from one of NCC peaks and unknown labels were attached to outliers so that small matching window can be used to gain high accuracy. Depth maps for the Cones data set were computed. \\ \cline{2-3}  & [PERSON] et al., 2012 [12] & DAISY descriptor matching. Dense point clouds were found from image pairs and only those points with the highest expected precision were retained. EPFL campus was reconstructed using 314 0-Megapixel images in 142 min and the final point cloud contains 11.3 million points. \\ \hline \multirow{7}{*}{Feature expansion algorithms} & [PERSON] et al., 1989 [13] & A “best-first” strategy adopted. 30 m Digital Elevation Model (DEM) was computed from a pair of 240 \(\times\) 240 terrain images with a mean difference of \(-\)37.16 m corresponding to matching accuracy possibly as low as 0.1 pixels. \\ \cline{2-3}  & [PERSON] et al., 2007 [14] & Window-based matching and expansion. A sequential expansion was performed based on a priority queue. Images were intelligently chosen at a per-view and per-pixel level. St. Peter’s cathedral was reconstructed from 151 Internet images by 50 photographers. \\ \cline{2-3}  & [PERSON] et al., 2007 [15] & Disk-based matching and expansion. New planar disks were expanded at a fraction (20\%) of the parent disk’s minimal radius (100 pixel). A statue of a Chinese warrior was reconstructed with 264k disks. \\ \cline{2-3}  & [PERSON] et al., 2010 [16] & Patch-based matching and expansion. All the patches were expanded with the same priority. Outliers were filtered out based on visibility and weak neighborhood regulation. Many small objects and large scenes were reconstructed. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A few selected works on 3D reconstruction based on photometry. Volume-based algorithms represent the space occupied by an object with voxels or a surrounding surface. Take the algorithm of [PERSON] et al. [PERSON] et al. (2005) in Table 1, for example, which discretizes a 3D scene into voxels and assigns colors to them, so as to achieve consistency with all the input images. To solve the occlusion problem, it decomposes the 3D scene into layers and sweeps them outward from the camera volume (the convex hull formed by the camera centers) in a single pass, with the constraint that no scene point is contained within the camera volume. This constraint on camera position was avoided by using a multi-sweep method, as suggested by [PERSON] et al. [PERSON] et al. (2006).