[image 03081] [CFP] ECCV Workshop: 3D Reconstruction in the Wild (3DRW2018)

[Ikuhisa Mitsugami]

image-ML/robotics-ML/CGVI-ML の皆様
（重複して受け取られた方はご容赦ください）

広島市立大学の満上です．
お世話になっております．

今年9月にミュンヘンで開催されるECCV2018の併設ワークショップとして，
三次元復元に関する以下のワークショップを開催いたします．

特徴抽出やSLAMから応用まで，実世界における三次元シーン理解に関する研究を
幅広く募集しておりますので，皆様からの多数のご投稿をお待ちしております．
締切は 7月17日 となっております．

どうぞよろしくお願いいたします．


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3D Reconstruction in the Wild (3DRW2018)
in conjunction with European Conference on Computer Vision (ECCV2018)
September 14th, 2018, Munich, Germany
http://www.sys.info.hiroshima-cu.ac.jp/3drw2018/

*** Submission Deadline: July 17th, 2018 [23:59 Pacific Time] ***

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CALL FOR PAPERS

Research on 3D reconstruction has long been focused on recovering 3D
information from multi-view images captured in ideal conditions. However,
the assumption of ideal acquisition conditions severely limits the
deployment possibilities for reconstruction systems, as typically 
several
external factors need to be controlled, intrusive capturing devices have 
to
be used or complex hardware setups need to be operated to acquire image
data suitable for 3D reconstruction. In contrast, 3D reconstruction in
unconstrained settings (referred to as 3D reconstruction in the wild)
usually imposes only little restrictions on the data acquisition 
procedure
and/or on data capturing environments, but, therefore, represents a far
more challenging task.

The goal of this workshop is to foster the development of 3D 
reconstruction
techniques that are robust and realtime, and consequently perform well 
on a
variety of environments with different characteristics.  Toward this 
goal,
we are interested in all parts of 3D reconstruction techniques ranging 
from
multi-camera calibration, feature extraction, matching, data fusion, 
depth
learning, and meshing techniques to 3D modeling approaches capable of
operating on image data captured in the wild. Topics of interest include,
but are not limited to:

Topics:

Various environments/extreme conditions
- 3D for agriculture, bio-imaging, and physics
- features from images in the heavy rain
- features from backlit images
- reconstruction of athletes in sports
- reconstruction of planets
- tracking in the snow
- underwater camera calibration, refractive concerns and

System/devices
- autonomous underwater navigation
- 3D from images captured by underwater cameras
- 3D from images captured using drones
- lighting/camera configuration
- mapping, localization and SLAM

Stereo algorithm and calibration
- 3D from unordered image sequences
- 3D from data (DNN and learning approach)
- depth from heavily incomplete data
- heavily distorted image matching
- structure from super-wide-baseline images
- structure from remote sensing images
- structure-from-motion and visual odometry
- reconstruction of thin objects

Geometry
- fusion for unreliable depth sequences
- geometry for unsynchronized multi-views
- mesh generation for fast deforming objects
- mesh interpolation for deforming objects

Others
- benchmarking dataset under challenging scenarios
- fusion for heterogeneous images
- phenotyping

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IMPORTANT DATES:
Submission: July 17th
Author notification: August 15th
Workshop: September 14th

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ORGANIZERS:
Akihiro Sugimoto, NII
Takeshi Masuda, AIST
Tomas Pajdla, Czech Technical University in Prague
Hiroshi Kawasaki, Kyusyu University
Shohei Nobuhara, Kyoto University
Hideo Saito, Keio University
Ikuhisa Mitsugami, Hiroshima City University
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