Marc Pollefeys presents Semantic 3D reconstruction
On 2016-04-12 - 2016-04-12 16:00:00 at G205, Karlovo náměstí 13, Praha 2
Obtaining 3D geometric information from images is one of the big challenges of
computer vision. It is critical for applications such as robotics, autonomous
vehicle navigation and augmented reality. I will first briefly talk about some
our recent work on vision-based autonomous micro-aerial vehicles, driverless
cars and 3D on mobile devices. While purely geometric models of the world can
be
sufficient for some applications, there are also many application that need
additional semantic information. In the second part, I will focus on 3D
reconstruction approaches which combine geometric and appearance cues to obtain
semantic 3D reconstructions. Specifically, the approaches I will discuss are
formulated as multi-label volumetric segmentation, i.e. each voxel gets
assigned
a label corresponding to one of the semantic classes considered, including
free-space. We propose a formulation representing raw geometric and appearance
data as unary or high-order (pixel-ray) energy terms on voxels, with
class-pair-specific learned anisotropic smoothness terms to regularize the
results. We will see how by solving both reconstruction and
segmentation/recognition jointly the quality of the results for both subtasks
can be improved and we can make significant progress towards 3D scene
understanding.
bio:
Marc Pollefeys is a full professor and head of the Institute for Visual
Computing of the Dept. of Computer Science of ETH Zurich which he joined in
2007. He leads the Computer Vision and Geometry lab. Previously he was with
the Dept. of Computer Science of the University of North Carolina at Chapel
Hill
where he started as an assistant professor in 2002 and became an associate
professor in 2005. Before he was a postdoctoral researcher at the Katholieke
Universiteit Leuven in Belgium, where he also received his M.S. and Ph.D.
degrees in 1994 and 1999, respectively. His main area of research is computer
vision. One of his main research goals is to develop flexible approaches to
capture visual representations of real world objects, scenes and events. Dr.
Pollefeys has received several prizes for his research, including a Marr prize,
an NSF CAREER award, a Packard Fellowship and a ERC Starting Grant. He is the
author or co-author of more than 250 peer-reviewed papers. He was a general
chair for ECCV 2014 in Zurich, was one of the program chairs for the IEEE Conf.
on Computer Vision and Pattern Recognition 2009. Prof. Pollefeys was on the
Editorial Board of the IEEE Transactions on Pattern Analysis and Machine
Intelligence, the International Journal of Computer Vision, Foundations and
Trends in Computer Graphics and Computer Vision and several other journals. He
is an IEEE Fellow.
computer vision. It is critical for applications such as robotics, autonomous
vehicle navigation and augmented reality. I will first briefly talk about some
our recent work on vision-based autonomous micro-aerial vehicles, driverless
cars and 3D on mobile devices. While purely geometric models of the world can
be
sufficient for some applications, there are also many application that need
additional semantic information. In the second part, I will focus on 3D
reconstruction approaches which combine geometric and appearance cues to obtain
semantic 3D reconstructions. Specifically, the approaches I will discuss are
formulated as multi-label volumetric segmentation, i.e. each voxel gets
assigned
a label corresponding to one of the semantic classes considered, including
free-space. We propose a formulation representing raw geometric and appearance
data as unary or high-order (pixel-ray) energy terms on voxels, with
class-pair-specific learned anisotropic smoothness terms to regularize the
results. We will see how by solving both reconstruction and
segmentation/recognition jointly the quality of the results for both subtasks
can be improved and we can make significant progress towards 3D scene
understanding.
bio:
Marc Pollefeys is a full professor and head of the Institute for Visual
Computing of the Dept. of Computer Science of ETH Zurich which he joined in
2007. He leads the Computer Vision and Geometry lab. Previously he was with
the Dept. of Computer Science of the University of North Carolina at Chapel
Hill
where he started as an assistant professor in 2002 and became an associate
professor in 2005. Before he was a postdoctoral researcher at the Katholieke
Universiteit Leuven in Belgium, where he also received his M.S. and Ph.D.
degrees in 1994 and 1999, respectively. His main area of research is computer
vision. One of his main research goals is to develop flexible approaches to
capture visual representations of real world objects, scenes and events. Dr.
Pollefeys has received several prizes for his research, including a Marr prize,
an NSF CAREER award, a Packard Fellowship and a ERC Starting Grant. He is the
author or co-author of more than 250 peer-reviewed papers. He was a general
chair for ECCV 2014 in Zurich, was one of the program chairs for the IEEE Conf.
on Computer Vision and Pattern Recognition 2009. Prof. Pollefeys was on the
Editorial Board of the IEEE Transactions on Pattern Analysis and Machine
Intelligence, the International Journal of Computer Vision, Foundations and
Trends in Computer Graphics and Computer Vision and several other journals. He
is an IEEE Fellow.