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We present the evaluation methodology, datasets and results of the BOP Challenge 2023, the fifth in a series of public competitions organized to capture the state of the art in model-based 6D object pose estimation from an RGB/RGB-D image and related tasks.
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PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes
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EPOS: Estimating 6D pose of objects with symmetries
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Keypoint cascade voting for point cloud based 6DoF pose estimation
Yangzheng Wu, Alireza Javaheri, Mohsen Zand, and Michael Greenspan · 2022
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3d model-based zero-shot pose estimation pipeline
Jianqiu Chen, Mingshan Sun, Tianpeng Bao, Rui Zhao, Liwei Wu, and Zhenyu He · 2023
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Ultralytics YOLO, Jan. 2023
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Geotransformer: Fast and robust point cloud registration with geometric transformer
Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Slobodan Ilic, Dewen Hu, and Kai Xu · 2023
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BOP challenge 2022 on detection, segmentation and pose estimation of specific rigid objects
Martin Sundermeyer, Tomas Hodan, Yann Labbé, Gu Wang, Eric Brachmann, Bertram Drost, Carsten Rother, and Jiri Matas · 2023
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GPose2023, a submission to the BOP Challenge 2023
Ruida Zhang, Ziqin Huang, Gu Wang, Xingyu Liu, Chenyangguang Zhang, and Xiangyang Ji · 2023
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Sam-6d: Segment anything model meets zero-shot 6d object pose estimation
Jiehong Lin, Lihua Liu, Dekun Lu, and Kui Jia · 2024
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Sungphill Moon, Hyeontae Son, Dongcheol Hur, and Sangwook Kim · 2024
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