To appear in Proceedings of Vision, Modelling and Visualization (VMV) 2009.
Authors: Savil Srivastava, Ashutosh Saxena, Christian Theobalt, Sebastian Thrun, Andrew Ng
Abstract:
We present i23, an algorithm to reconstruct a 3D model from a single image taken with an arbitrary photo camera. It is based off an automatic machine learning approach that casts 3D reconstruction as a probabilistic inference problem using a Markov Random Field trained on ground truth data.
Since it is difficult to learn statistical relations for all possible images, the quality of the automatic reconstruction is sometimes unsatisfying. We therefore designed an intuitive interface for a user to sketch, in a few seconds, additional hints to the algorithm. We have developed a way to incorporate these constraints into the probabilistic reconstruction framework in order to obtain 3D reconstructions of much higher quality than previous
fully-automatic methods. Our system also represents an exciting new computational photography tool, enabling
new ways of rendering and editing of photos.
Related Work:
Builds off Ashutosh Saxena's Make3d algorithm (http://make3d.stanford.edu)
