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Open AccessArticle

RobotP: A Benchmark Dataset for 6D Object Pose Estimation

Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The Netherlands
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Academic Editor: Anastasios Doulamis
Sensors 2021, 21(4), 1299; https://doi.org/10.3390/s21041299
Received: 20 December 2020 / Revised: 3 February 2021 / Accepted: 7 February 2021 / Published: 11 February 2021
(This article belongs to the Section Intelligent Sensors)
Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms. View Full-Text
Keywords: benchmark dataset; 6D pose estimation; sensors; 3D reconstruction benchmark dataset; 6D pose estimation; sensors; 3D reconstruction
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MDPI and ACS Style

Yuan, H.; Hoogenkamp, T.; Veltkamp, R.C. RobotP: A Benchmark Dataset for 6D Object Pose Estimation. Sensors 2021, 21, 1299. https://doi.org/10.3390/s21041299

AMA Style

Yuan H, Hoogenkamp T, Veltkamp RC. RobotP: A Benchmark Dataset for 6D Object Pose Estimation. Sensors. 2021; 21(4):1299. https://doi.org/10.3390/s21041299

Chicago/Turabian Style

Yuan, Honglin; Hoogenkamp, Tim; Veltkamp, Remco C. 2021. "RobotP: A Benchmark Dataset for 6D Object Pose Estimation" Sensors 21, no. 4: 1299. https://doi.org/10.3390/s21041299

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