The Robotic Vision Scene Understanding Challenge
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 2; 2025: 2.
9 papers.
- 2022Retrospectives on the Embodied AI Workshopcited by 46
- 2021A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Segmentation of Indoor Scenescited by 12
- 2024Semantic Map-based Generation of Navigation Instructionscited by 6
- 2023Moving Forward by Moving Backward: Embedding Action Impact over Action Semanticscited by 3
- 2025Tactile MNIST: Benchmarking Active Tactile Perceptioncited by 2
- 2023Effective Baselines for Multiple Object Rearrangement Planning in Partially Observable Mapped Environmentscited by 1
- 20243D Semantic MapNet: Building Maps for Multi-Object Re-Identification in 3Dcited by 1
- 2025A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methodscited by 1
- 2021Indoor Semantic Scene Understanding using Multi-modality Fusioncited by 0
- 2025A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods1 Jul 2025
- 2025Tactile MNIST: Benchmarking Active Tactile Perception3 Jun 2025
- 2024Semantic Map-based Generation of Navigation Instructions28 Mar 2024
- 20243D Semantic MapNet: Building Maps for Multi-Object Re-Identification in 3D19 Mar 2024
- 2023Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics24 Apr 2023
- 2023Effective Baselines for Multiple Object Rearrangement Planning in Partially Observable Mapped Environments24 Jan 2023
- 2022Retrospectives on the Embodied AI Workshop13 Oct 2022
- 2021Indoor Semantic Scene Understanding using Multi-modality Fusion17 Aug 2021
- 2021A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Segmentation of Indoor Scenes11 Aug 2021