MIT Media Lab, E14-240
In this talk, Srikumar Ramalingam will show an efficient method for reconstructing the 3D arrangement of lines extracted from a single image, using vanishing points, orthogonal structure, and an optimization procedure that considers all plausible connectivity constraints between lines. Line detection identifies a large number of salient lines that intersect or nearly intersect in an image, but relatively a few of these apparent junctions correspond to real intersections in the 3D scene. We use linear programming (LP) to identify a minimal set of least-violated connectivity constraints that are sufficient to unambiguously reconstruct the 3D lines. In contrast to prior solutions that primarily focused on well-behaved synthetic line drawings with severely restricting assumptions, we develop an algorithm that can work on real images. The algorithm produces line reconstruction by identifying 95 percent correct connectivity constraints in York Urban database, with a total computation time of one second per image.
Srikumar Ramalingam is a senior principal research scientist at Mitsubishi Electric Research Lab (MERL) since 2008. He received a Marie Curie VisionTrain scholarship from European Union to pursue his studies at INRIA Rhone Alpes (France) and he obtained his PhD in 2007. His thesis on generic imaging models received INPG best thesis prize and AFRIF thesis prize (honorable mention) from the French Association for Pattern Recognition. He has published numerous papers in flagship conferences such as CVPR, ICCV, SIGGRAPH ASIA and ECCV. He has coauthored books, given tutorials, and organized workshops on topics such as multi-view geometry and graphical models. His research interests are in computer vision, machine learning, robotics, and autonomous driving.
Host/Chair: Camera Culture