[visionlist] [meetings][CfP] Robotic Vision Scene Understanding (RVSU) Challenge
d20.hall at qut.edu.au
Tue Mar 2 01:28:09 -04 2021
Call for Participants - Robotic Vision Scene Understanding (RVSU) Challenge
This is a call for participants for the latest ACRV robotic vision scene understanding (RVSU) challenge.
This challenge is being run as one of multiple embodied AI challenges in the CVPR2021 Embodied AI Workshop.
Eval AI Challenge Link: https://eval.ai/web/challenges/challenge-page/807/overview
Challenge Overview Webpage: http://cvpr2021.roboticvisionchallenge.org/
Workshop Webpage: https://embodied-ai.org/
Deadline: May 7th
Prizes: Total of $2500 USD, 2 Titan RTX GPUs and up to 10 Jetson Nano GPUs to be distributed (details below)
The Robotic Vision Scene Understanding Challenge evaluates how well a robotic vision system can understand the semantic and geometric aspects of its environment. The challenge consists of two distinct tasks: Object-based Semantic SLAM, and Scene Change Detection.
Key features of this challenge include:
* BenchBot, a complete software stack for running semantic scene understanding algorithms.
* Running algorithms in realistic 3D simulation, and on real robots, with only a few lines of Python code.
* Tiered difficulty levels to allow for easy of entry to robotic vision with embodied agents and enable ablation studies.
* The BenchBot API, which allows simple interfacing with robots and supports OpenAI Gym-style approaches and a simple object-oriented Agent approach.
* 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, which uses the same API as the simulated robot.
* Use of the Nvidia Isaac SDK for interfacing with, and simulation of, high fidelity 3D environments.
Object-based Semantic SLAM: Participants use a robot to traverse around the environment, building up an object-based semantic map from the robot’s RGBD sensor observations and odometry measurements.
Scene Change Detection: Participants use a robot to traverse through an environment scene, building up a semantic understanding of the scene. Then the robot is moved to a new start position in the same environment, but with different conditions. Along with a possible change from day to night, the new scene has a number objects added and / or removed. Participants must produce an object-based semantic map describing the changes between the two scenes.
Difficulty Levels: We provide three difficulty levels of increasing complexity and similarity to true active robotic vision systems. At the simplest difficulty level (PGT), the robot moves to pre-defined poses to collect data and provides ground-truth poses, removing the need for active exploration and localization . The second level (AGT) requires active exploration and robot control but still provides ground-truth pose to remove localization requirements. The final mode (ADR) is the same as the previous but provides only noisy odometry information, requiring localization to be calculated by the system.
As the challenge is complex, with multiple components, we provide a tiered prize list. The highest scoring on any given leaderboard will be awarded the corresponding prize. Teams are allowed to participate across all challenges and win multiple prizes.
1. Scene Change Detection (ADR) - $900 USD, 1 Titan RTX GPU, up to 5 Jetson Nano GPUs
2. Semantic SLAM (ADR) - $800 USD, 1 Titan RTX GPU, up to 5 Jetson Nano GPUs
3. Semantic SLAM (AGT) - $500 USD
4. Semantic SLAM (PGT) - $300 USD
E-mail: contact at roboticvisionchallenge.org
Partners and embodied AI challenges at CVPR 2021:
* iGibson Challenge 2021, hosted by Stanford Vision and Learning Lab and Robotics at Google (https://svl.stanford.edu/igibson/challenge.html)
* Habitat Challenge 2021, hosted by Facebook AI Research (FAIR) and Georgia Tech (https://aihabitat.org/challenge/2020/)
* Navigation and Rearrangement in AI2-THOR, hosted by the Allen Institute for AI (https://ai2thor.allenai.org/rearrangement)
* ALFRED: Interpreting Grounded Instructions for Everyday Tasks, hosted by the University of Washington, Carnegie Mellon University, the Allen Institute for AI, and the University of Southern California (https://askforalfred.com/EAI21/)
* Room-Across-Room Habitat Challenge (RxR-Habitat), hosted by Oregon State University, Google, and Facebook AI (https://ai.google.com/research/rxr/habitat)
* SoundSpaces Challenge, hosted by the University of Texas at Austin and the University of Illinois at Urbana-Champaign (https://soundspaces.org/challenge)
* TDW-Transport, hosted by the Massachusetts Institute of Technology (https://tdw-transport.csail.mit.edu/)
* Robotic Vision Scene Understanding, hosted by the Australian Centre for Robotic Vision in association with the Queensland University of Technology Centre for Robotics (https://nikosuenderhauf.github.io/roboticvisionchallenges/scene-understanding)
* MultiON: Multi-Object Navigation, hosted by the Indian Institute of Technology Kanpur, the University of Illinois at Urbana-Champaign, and Simon Fraser University (https://aspis.cmpt.sfu.ca/projects/multion/)
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