[visionlist] [ICCV'19 tutorial] Global Optimization for Geometric Understanding with Provable Guarantees

[Tat-Jun Chin]

Dear colleagues,



We cordially invite you to attend the ICCV’19 tutorial on *Global
Optimization for Geometric Understanding with Provable Guarantees.*



The tutorial will be held in the morning of Sunday, October 27 in room #402
(first day of ICCV!).



We will discuss novel optimization tools for robust 3D vision (particularly
important for safety critical robotics applications), provide a snapshot of
the state of the art, and outline a number of open problems. We trust this
will be a useful event, with multiple talks, hands-on examples, and open
discussion. Please find more details below:



------------------------------------------------------------------------------------

*Global Optimization for Geometric Understanding with Provable Guarantees*
 (tutorial)

International Conference on Computer Vision (ICCV)

Date: October 27, 2019, Seoul

Website: http://globalOptimization-ICCV2019.mit.edu
<http://globaloptimization-iccv2019.mit.edu/>

Room: #402



*OVERVIEW:*

*----------------*

This tutorial aims to give an in-depth introduction to global optimization
tools, including convex and semidefinite relaxations, applied to 3D vision
problems. The first goal of the tutorial is to motivate the need for global
solvers by providing real-world examples where the lack of robustness
results from the difficulty in solving large optimization problems to
optimality. The second goal is to provide the attendees with basic
mathematical and algorithmic concepts and survey important recent advances
in the area. The third goal is to outline several open research avenues:
global optimization has an enormous untapped potential and it is hoped that
this tutorial will inspire researchers to use modern optimization tools to
solve several outstanding challenges in geometric vision.



*DETAILED DESCRIPTION:*


*------------------------------------*Understanding the geometry of a scene
from camera observations is a key requirement from many applications,
ranging from autonomous vehicles (e.g., self-driving cars, consumer drones)
to Virtual and Augmented Reality. Indeed, geometric understanding
encompasses several core topics in computer vision and robotics, including
Structure from Motion and SLAM, point cloud registration and object pose
estimation, single-view and two-view geometry, among many others. Despite
the maturity of the algorithms developed for geometric understanding, both
researchers and practitioners are well aware that the presence of large
noise, outliers, and missing data makes modern pipelines brittle when
operating in the wild. An incorrect understanding of the geometry of the
scene can negatively affect the user experience in consumer applications,
and may put human life at risk in safety-critical applications, such as
self-driving cars.

One of the main causes of the fragility of geometric understanding is the
fact that modern pipelines typically trade-off performance (robustness,
optimality, guarantees) for computational efficiency. Indeed, many
optimization problems underlying geometric understanding are intractable
due to non-convexity or due to their combinatorial nature (e.g.,
measurement selection to reject potential outliers). Several algorithms
obtain fast solutions using local iterative nonlinear solvers or heuristic
approaches. While heuristics and local solvers tend to work well in the
low-noise low-outlier regime, they are prone to fail in more challenging
real-world conditions.

Global optimization and convex relaxation techniques have been recently
shown to provide an effective tool to tame the complexity of geometric
understanding while enabling efficient solutions. A sequence of papers have
debunked two common misconceptions behind these methods. The first
misconception is that a convex relaxation always entails computing an
“approximate solution”: indeed recent papers have shown that, in certain
regimes, one can design convex relaxations that (provably) solve the
original problem exactly. The second misconception is that many of these
relaxations, in particular semidefinite relaxations, are slow in practice:
modern solvers and theory instead show that one can solve several classes
of such relaxations very efficiently. A growing body of work in vision and
robotics demonstrates that these tools can indeed solve in a provably
optimal manner problems including rotation averaging and SLAM,
registration, and combinatorial problems involving outlier rejection.

This tutorial aims to give an in-depth introduction to global optimization
tools, including convex and semidefinite relaxations. The first goal of the
tutorial is to motivate the need for global solvers by providing real-world
examples where the lack of robustness results from the difficulty in
solving large optimization problems to optimality. The second goal is to
provide the attendees with basic mathematical and algorithmic concepts, and
survey important recent advances in the area. The third goal is to outline
several open research avenues: global optimization has an enormous untapped
potential and it is hoped that this tutorial will inspire researchers to
use modern optimization tools to solve several outstanding challenges in
geometric vision.



*ORGANIZING COMMITTEE:*

*-------------------------------------*

- Luca Carlone, Massachusetts Institute of Technology

- Tat-Jun Chin, The University of Adelaide

- Anders Eriksson, University of Queensland

- Heng Yang, Massachusetts Institute of Technology

- Fredrik Kahl, Chalmers University of Technology





*FURTHER INFORMATION:*

*-----------------------------------*

Please send any questions to Luca Carlone (lcarlone at mit.edu), Tat-Jun Chin (
tat-jun.chin at adelaide.edu.au), Heng Yang (hankyang at mit.edu), or Anders
Eriksson (a.eriksson at uq.edu.au).
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