June 2nd, 2020 
Daniela Lopez de Luise Call for Participants: Robotic Vision Scene Understanding Challenge
The Australian Centre for Robotic Vision is pleased to announce a new robotic vision challenge on scene understanding. In our challenge, participants will control a robotic agent using simple OpenAI Gym-style controls within a virtual environment to map out the cuboid locations of objects in 3D space. A cash prize of $2,500 USD will be split among high-performing participants in our challenge and they will also receive the opportunity to run their scene understanding algorithms, with no modifications, on a real robotic platform.
The Australian Centre for Robotic Vision is pleased to announce a new robotic vision challenge on scene understanding. In our challenge, participants will control a robotic agent using simple OpenAI Gym-style controls within a virtual environment to map out the cuboid locations of objects in 3D space. A cash prize of $2,500 USD will be split among high-performing participants in our challenge and they will also receive the opportunity to run their scene understanding algorithms, with no modifications, on a real robotic platform.
Challenge Link: https://evalai.cloudcv.org/web/challenges/challenge-page/625
Important Dates
Important Dates
- Final submissions to EvalAI Due – 2nd September 2020
- Accompanying Paper submissions – 2nd October 2020
- Result notifications – 16th October 2020
Overview
The Robotic Vision Scene Understanding Challenge evaluates how well a robotic vision system can understand the semantic and geometric aspects of its environment. There are two tasks in this challenge: Object-based Semantic Mapping/SLAM, and Scene Change Detection.
The Robotic Vision Scene Understanding Challenge evaluates how well a robotic vision system can understand the semantic and geometric aspects of its environment. There are two tasks in this challenge: Object-based Semantic Mapping/SLAM, and Scene Change Detection.
Semantic SLAM: Participants use a robot to traverse around the environment, building up an object-based semantic map.
Scene change detection (SCD): Participants use a robot to traverse through two different instances of an environment. Between instances some objects are added or removed and participants must produce an object-based semantic map describing the changes between scenes.
Each task has three difficulty levels with lowest difficulty level requiring no active navigation or localization from the participant, the next level requiring navigation but not localization, and the highest level requiring both as the simulation becomes more akin to a real robot.
Other key features of the challenge include:
- BenchBot a complete software stack for running semantic scene understanding algorithms
- The BenchBot API allowing simple interfacing with robots, supporting OpenAI Gym-style approaches
- Running algorithms in realistic 3D simulation powered by Nvidia's Isaac simulator, and on real robots, with only a few lines of Python code
- Easy-to-use-scripts for running simulated environments, executing code on a simulated robot, evaluating semantic scene understanding results, and automating code execution across multiple environments
- Opportunities for the best teams to execute their code on a real robot in our lab
More Information:
Challenge server: https://evalai.cloudcv.org/web/challenges/challenge-page/625
BenchBot software stack: http://benchbot.org
Challenge overview video: https://youtu.be/jQPkV29KFv
Contact Us:
e-mail: contact@roboticvisionchallenge.org
Website: http://roboticvisionchallenge.org
Slack Workspace: roboticvision-hmc7922.slack.com
Twitter: @robVisChallenge
e-mail: contact@roboticvisionchallenge.org
Website: http://roboticvisionchallenge.org
Slack Workspace: roboticvision-hmc7922.slack.com
Twitter: @robVisChallenge
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