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RT-less: a multi-scene RGB dataset for 6D pose estimation of reflective texture-less objects

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Abstract

The 6D (6 Degree of freedom) pose estimation (or pose measurement) of machined reflective texture-less objects, which are common in industry, is a significant but challenging technique. It has attracted increasing attention in academia and industry. However, it is difficult to obtain suitable public datasets of such objects, which makes relevant studies inconvenient. Thus, we proposed the Reflective Texture-Less (RT-Less) object dataset, which is a new public dataset of reflective texture-less metal parts for pose estimation research. The dataset contains 38 machined texture-less reflective metal parts in total. Different parts demonstrate the symmetry and similarity of shape and size. The dataset contains 289 K RGB images and the same number of masks, including 25,080 real images, 250,800 synthetic images in the training set, and 13,312 real images captured in 32 different scenes in the test set. The dataset also provides accurate ground truth poses, bounding-box annotations and masks for these images, which makes RT-Less suitable for object detection and instance segmentation. To improve the accuracy of the ground truth, an iterative pose optimization method using only RGB images is proposed. Baselines of the state-of-the-art pose estimation methods are provided for further comparative studies. The dataset and results of baselines are available at: http://www.zju-rtl.cn/RT-Less/.

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Data availability

The data and necessary code is available at http://www.zju-rtl.cn/RT-Less/.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 52275514 and 52275547, and in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LY21E050021.

Funding

National Natural Science Foundation of China, 52275514, Zaixing He, 52275547, Xinyue Zhao, Natural Science Foundation of Zhejiang Province, LY21E050021, Xinyue Zhao.

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Authors and Affiliations

  1. The School of Mechanical Engineering, The State Key Lab of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China

    Xinyue Zhao, Quanzhi Li, Yue Chao, Quanyou Wang & Zaixing He

  2. The College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Dong Liang

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  1. Xinyue Zhao
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Correspondence to Zaixing He.

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Xinyue Zhao, Quanzhi Li, Yue Chao, Quanyou Wang, Zaixing He, and Dong Liang declare that they have no conflict of interest or financial conflicts to disclose. The authors declares that they have no conflict of interest.

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Zhao, X., Li, Q., Chao, Y. et al. RT-less: a multi-scene RGB dataset for 6D pose estimation of reflective texture-less objects. Vis Comput 40, 5187–5200 (2024). https://doi.org/10.1007/s00371-023-03097-1

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