Multiview stereo reconstruction methods can provide impressive results in a number of applications. Nevertheless, when trying to apply the state-of-the-art methods in the case of a more structured 3D acquisition, the lack of feedback on the quality of the reconstruction during the photo shooting can be problematic. In this paper we present a framework for the assisted reconstruction from images of real objects. The framework is able to provide, in quasi-real time, a sparse reconstruction of the scene, so that the user is able to spot the missing or problematic parts. Moreover, the framework is able to separate the object of interest from the background and suggests missing points of view to the user, without any previous knowledge of the shape of the scene and the acquisition path. This is obtained by analyzing the sparse reconstruction and the connection between the reconstructed points and the input images. The framework has been tested on a variety of practical cases, and it has proved to be effective not only to obtain more complete reconstructions, but also to reduce the number of images needed and the processing time for dense reconstruction.