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Workshop Description and Motivation

One of the major success stories of Computer Vision over the 1990s and 2000s was the development of systems that can simultaneously build a map of the environment and localize a camera with respect to that environment. Within a batch framework this is usually known as structure from motion or multi-view reconstruction, and systems that can reconstruct city-scale (or even wider-scale) environments with accurate geometry are now common, whether from video or from a less well ordered set of images.  Within the robotics and real-time vision communities this problem is often referred to as visual SLAM, Simultaneous Localization and Mapping.  The current state-of-the-art visual SLAM systems can reconstruct large areas either densely or semi-densely (i.e. with a depth estimate at all or many pixels) with high accuracy using just a single camera, in real-time.

As impressive as these systems and algorithms are, they have no understanding of the scenes they observe – at best they provide a dense geometric point-cloud – and fall well short of the ability of the human visual system to assimilate high-level visual information.  In contrast to much of the work in multi-view analysis, those working with single images have made significant progress in applications such as segmentation and object and place recognition, so that an image can be labelled with high level designations of its content, explaining not just the geometry, but also the semantic content of the image.  Nevertheless these algorithms are often slow, require offline learning that does not necessarily adapt or transfer between environments, and importantly have no temporal component. 

Within the last few years a number of researchers in computer vision and robotics have recognised the benefits that applying this semantic level analysis to image sequences acquired by a moving camera, proposing a shift from purely geometric reconstruction and mapping, to semantic level descriptions of scenes involving objects, surfaces, attributes and scene relations that together capture an understanding of the scene. This shift will enable recognition of long-term change through maps that are more versatile, informative, and compact and so it is likely that through advances in this area the quest for long-term autonomy can make greatest gains. 

This workshop will bring together interested researchers in multi-view geometry, scene understanding and robotic vision with the common goal of discussing and presenting the state-of-the-art in the use of semantics in structure from motion and robotic vision, and in considering the most fruitful and challenging areas for the development of the field.  The workshop will cmpirse invited talks, and a limited number of submitted talks, selected for presentation by the organising committee.  Topics to be addressed will include:

  • Representations of large-scale 3D scenes
  • The roles of context, attributes and affordances in scene understanding
  • Coping with dynamic scenes and recognizing and representing change
  • Real-time semantics and scene understanding
  • Task-driven scene understanding and reinterpretation of semantics
  • Appropriate learning mechanisms: online learning;  the balance of stability and plasticity required for life-long learning; the role of one-shot learning and human-in-the-loop approaches

Important dates

  • Extended abstract (2-4 pages) due 19th April
  • Notification of acceptance, 1st May
  • Workshop, Thurs 11th June 2015 (full day)

Submissions

  • The workshop solicits high quality extended abstracts for (non-blind) review by a program committee comprising the organisers and invited speakers.  Abstracts can be submitted by e-mail to ian.reid@adelaide.edu.au and should follow the usual CVPR guidelines for style and be between 2 and 4 pages long (including references). The abstract need not describe original contributions (they may, for example, describe recently published important results), but should be recent, relevant and thought-provoking.  The abstracts will be reviewed by the members of the program committee and the most significant contributions accepted for a 15min oral presentation. 

Invited speakers

The following are confirmed invited speakers:

  • Prof. John Leonard, MIT
  • A/Prof. Raquel Urtasun, University of Toronto
  • A/Prof. Ashutosh Saxena, Cornell University
  • Dr Richard Newcombe, U of Washington
  • Prof Martial Hebert, CMU
  • Prof Marc Pollefeys, ETHZ

Program

The program is tentative, but indicative

Time
Program Item
9.00Welcome and Introduction
9.15Invited talk: Martial Hebert, Semantically-referenced navigation
9.50Invited talk: Raquel Urtasun, Exploiting the Web for Reconstruction, Recognition and Self-localization
10:25Arsalan Mousavian and Jana Kosecka, Semantically Aware Bag-of-Words for Localization
10.40Coffee Break
11.00Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent and Roberto Cipolla, SynthCam: Semantic Understanding With Synthetic Indoor Scenes
11:15Yinda Zhang, Shuran Song, Ping Tan and Jianxiong Xiao, PanoContext: A Whole-room 3D Context Model for Panoramic Scene Understanding
11.30Invited talk: John Leonard, Challenges for Life-Long Visual Mapping and Navigation
12:05Invited talk: Ashutosh Saxena, RoboBrain: Large-Scale Knowledge Engine for Robots
12.40Lunch
13:40Cancelled: Richard Newcombe, What do we hope to achieve with visual SLAM?
14:05Fisher Yu, Jianxiong Xiao and Thomas Funkhouser, Semantic Alignment of LiDAR Data at City Scale
14:20Hayko Riemenschneider, Andras Bodis-Szomoru, Andelo Martinovic, Julien Weissenberg, Luc Van Gool, What is needed for Multi-view Semantic Segmentation?
14:50Coffee break
15:10Jinglu Wang, Showei Li, Jongbo Liu, Honghui Zhang, Tian Fang, Siyu Zhu, Punze Zhang, Shengnan Cai and Long Quan, Semantic Segmentation of Large-scale Urban 3D data with Low Annotation Cost
15:25Invited talk: Marc Pollefeys, Semantic 3D Reconstruction
16:00Discussion / wrap-up
16:30Close

Organisers

Sponsors

  • This workshop is sponsored by the Australian Research Council Centre of Excellence in Robotic Vision

 

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