Robust 3D SFS reconstruction based on reliability maps

This paper deals with Shape from Silhouette (SfS) volumetric reconstruction in the context of multi-view smart room scenarios. The method that we propose first computes a 2D foreground object segmentation in each one of the views, by using region-based models to model the foreground, and shadow classes, and a pixel-wise model to model the background class. Next, we calculate the reliability maps between foreground and background/shadow classes in each view, by computing the hellinger distance among models. These 2D reliability maps are taken into account finally, in the 3D SfS reconstruction algorithm, to obtain an enhanced final volumetric reconstruction. The advantages of our system rely on the possibility to obtain a volumetric representation which automatically defines the optimal tolerance to errors for each one of the voxels of the volume, with a low rate of false positive and false negative errors. The results obtained by using our proposal improve the traditional SfS reconstruction computed with a fixed tolerance for the overall volume.