Lifting 3D Manhattan Lines from a Single Image
Abstract:
In the first part of the talk, I will present a novel and 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% correct connectivity constraints in York Urban database, with a total computation time of 1 second per image.
In the second part of the talk, I will briefly mention about my other work in graphical models, robotics, geo-localization, generic camera modeling and 3D reconstruction.
Brief Bio:
Srikumar Ramalingam is a Principal Research Scientist at Mitsubishi Electric Research Lab (MERL). He received his B.E from Anna University (Guindy) in India and his M.S from University of California (Santa Cruz) in USA. He received a Marie Curie Fellowship 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. After his PhD, he spent two years in Oxford working as a research associate in Oxford Brookes University, while being an associate member in visual geometry group in Oxford University. He has published more than 30 papers in flagship conferences such as CVPR, ICCV, SIGGRAPH ASIA and ECCV. He has co-edited journals, coauthored books, given tutorials and organized workshops on topics such as multi-view geometry and discrete optimization. His research interests are in computer vision, machine learning and robotics problems.
