Workshop on Learning to See from 3D Data

in conjunction with ICCV2017

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Location:

Sala Perla (Palazzo del Casino)


Mission:

Recently, large-scale repositories such as ShapeNet have made available 3D geometry data with properties such as texture, object part annotations, orientation alignment, and object semantics.

The geometric and material information in these repositories has catalyzed machine learning techniques for understanding the real world. In particular, during the past few years, we have witnessed pioneering work published at ICCV, ECCV, CVPR, and NIPS that leverages learning from big 3D data, on a broad range of topics including 3D reconstruction from single images, novel-view synthesis, 3D shape space learning, 3D shape completion, and joint 2D image and 3D shape analysis.

We propose to organize this workshop in order to coordinate the effort of the research community on tackling these problems. The workshop is comprised of two parts. One part will present results from an upcoming ShapeNet-related challenge on Large-scale 3D Understanding and Synthesis Challenges. The other part will consist of invited talks by leading researchers on various topics related to learning from big 3D data, especially synthetic data. Next, we discuss the plans for both parts in details.


Speakers:

Jitendra Malik
UC Berkeley
Thomas Brox
University of Freiburg
Manmohan Chandraker
UC San Diego
Thomas Funkhouser
Princeton University
Andreas Geiger
Max Planck Institute
Jianxiong Xiao
AutoX
Kristen Grauman
UT Austin
Kate Saenko
Boston University


Important Dates:

For the important days of ShapeNet Challenge, please check the Schedule section here

Publication:

Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55, Li Yi, Hao Su, Lin Shao, Manolis Savva, Haibin Huang, Evangelos Kalogerakis, Thomas Funkhouser, Leonidas Guibas, et al. PDF

This report summarizes the results from ShapeNet challenge, including an overview of methods from participating teams, their performances, and discussions.


Schedules (tentative):

8:50-9:00

Opening Remark

9:00-9:30

Keynote (Manmohan Chandraker)
Title: TBD

9:30-10:00

Keynote (Kristen Grauman)
Title: Intelligent look-around behavior: Learning to examine 3D objects and scenes

10:00-10:15

Coffee break

10:15-10:30

Challenge introduction and summarization

10:30-11:00

Segmentation Challenge Winner Talk

11:00-11:30

Reconstruction Challenge Winner Talk

11:30-13:30

Lunch

13:30-14:00

Keynote (Thomas Funkhouser)
Title: Learning from 3D Data in Indoor Scenes

14:00-14:30

Keynote (Kate Saenko)
Title: Adapting Deep Models across Visual Domains

14:30-15:00

Keynote (Jitendra Malik)
Title: TBD

15:00-15:15

Coffee Break

15:15-15:45

Keynote (Thomas Brox)
Title: Learning to see 3D from single images

15:45-16:15

Keynote (Andreas Geiger)
Title: Deep Models for 3D Reconstruction

16:15-16:45

Keynote (Jianxiong Xiao)
Title: Perception and Interaction in 3D