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The Robotic Vision Scene Understanding Challenge

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arxiv 2009.05246 v1 pith:J2CYKR2U submitted 2020-09-11 cs.RO

classification cs.RO
keywords sceneunderstandingactiverobotroboticvisionagencychallenge
verification ladder T0 review T1 audit T2 compute T3 formal
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Being able to explore an environment and understand the location and type of all objects therein is important for indoor robotic platforms that must interact closely with humans. However, it is difficult to evaluate progress in this area due to a lack of standardized testing which is limited due to the need for active robot agency and perfect object ground-truth. To help provide a standard for testing scene understanding systems, we present a new robot vision scene understanding challenge using simulation to enable repeatable experiments with active robot agency. We provide two challenging task types, three difficulty levels, five simulated environments and a new evaluation measure for evaluating 3D cuboid object maps. Our aim is to drive state-of-the-art research in scene understanding through enabling evaluation and comparison of active robotic vision systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Tactile MNIST: Benchmarking Active Tactile Perception

    cs.RO 2025-06 conditional novelty 6.0 of 10

    The authors release a Gymnasium-compatible benchmark with four active tactile tasks, 13,580 3D digit models, and 153,600 real touches on 600 printed digits.

  2. A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods

    cs.RO 2025-07 unverdicted novelty 2.0 of 10

    A robotics-focused review of sound source localization research, emphasizing deep learning architectures, datasets, and open challenges.

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