Optics and Precision Engineering, Volume. 32, Issue 6, 901(2024)

Object 6-DoF pose estimation using auxiliary learning

Minjia CHEN1...2, Shaoyan GAI1,2,*, Feipeng DA1,2, and Jian YU1,23,* |Show fewer author(s)
Author Affiliations
  • 1School of Automation, Southeast University, Nanjing20096, China
  • 2Key Laboratory of Measurement and Control of Complex Engineering Systems, Ministry of Education, Southeast University, Nanjing10096, China
  • 3Key Laboratory of Space Photoelectric Detection and Perception, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing211106, China
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    References(29)

    [1] Z H WANG, X Y SUN, H WEI et al. Enhancing 6-DoF object pose estimation through multiple modality fusion: a hybrid CNN architecture with cross-layer and cross-modal integration. Machines, 11, 891(2023).

    [2] R SONG, J J LI et al. Rigidity-aware detection for 6D object pose estimation, 8927-8936(2023).

    [3] F DUFFHAUSS, S KOCH, H ZIESCHE et al. SyMFM6D: symmetry-aware multi-directional fusion for multi-view 6D object pose estimation. IEEE Robotics and Automation Letters, 8, 5315-5322(2023).

    [4] J LIEBELT, C SCHMID, K SCHERTLER. Viewpoint-Independent object class detection using 3D feature maps, 1-8(2008).

    [5] D G LOWE. Object recognition from local scale-invariant features, 1150-1157(1999).

    [6] B DROST, M ULRICH, N NAVAB et al. Model globally, match locally: efficient and robust 3D object recognition, 998-1005(2010).

    [7] [7] 郭清达, 全燕鸣, 姜长城, 等. 应用摄像机位姿估计的点云初始配准[J]. 光学 精密工程, 2017, 25(6): 1635. doi: 10.3788/ope.20172506.1635GUOQ D, QUANY M, JIANGC C, et al. Initial registration of point clouds using camera pose estimation[J]. Opt. Precision Eng., 2017, 25(6): 1635.(in Chinese). doi: 10.3788/ope.20172506.1635

    [8] Y S HE, H B HUANG, H Q FAN et al. FFB6D: a full flow bidirectional fusion network for 6D pose estimation, 3002-3012(2021).

    [9] M Z IRSHAD, S ZAKHAROV, R AMBRUS et al. SHAPO: implicit representations for multi-object shape, appearance, and pose optimization. Israel, 275-292(2022).

    [10] N K MO, W S GAN, N YOKOYA et al. ES6D: a computation efficient and symmetry-aware 6D pose regression framework, 6708-6717(2022).

    [11] [11] 周佳乐, 朱兵, 吴芝路. 融合二维图像和三维点云的相机位姿估计[J]. 光学 精密工程, 2022, 30(22): 2901-2912. doi: 10.37188/ope.20223022.2901ZHOUJ L, ZHUB, WUZ L. Camera pose estimation based on 2D image and 3D point cloud fusion[J]. Opt. Precision Eng., 2022, 30(22): 2901-2912.(in Chinese). doi: 10.37188/ope.20223022.2901

    [12] C WANG, D F XU, Y K ZHU et al. DenseFusion: 6D object pose estimation by iterative dense fusion, 3338-3347(2019).

    [13] Y S HE, W SUN, H B HUANG et al. PVN3D: a deep point-wise 3D keypoints voting network for 6DoF pose estimation, 11629-11638(2020).

    [15] W T HUA, Z X ZHOU, J WU et al. REDE: end-to-end object 6D pose robust estimation using differentiable outliers elimination. IEEE Robotics and Automation Letters, 6, 2886-2893(2021).

    [17] [17] 翟敬梅, 黄乐. 堆叠散乱目标的6D位姿估计和无序分拣[J]. 哈尔滨工业大学学报, 2022, 54(7): 136-142. doi: 10.11918/202110081ZHAIJ M, HUANGL. 6D pose estimation and unordered picking of stacked cluttered objects[J]. Journal of Harbin Institute of Technology, 2022, 54(7): 136-142.(in Chinese). doi: 10.11918/202110081

    [18] Z P ZHANG, P LUO, C C LOY et al. Facial Landmark Detection by Deep Multi-Task Learning. Computer Vision-ECCV 2014, 94-108(2014).

    [19] X P LIU, N XUE, T F WU. Learning auxiliary monocular contexts helps monocular 3D object detection. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 1810-1818(2022).

    [20] W KEHL, F MANHARDT, F TOMBARI et al. SSD-6D: making RGB-Based 3D detection and 6D pose estimation great again, 1530-1538(2017).

    [21] Y XIANG, T SCHMIDT, V NARAYANAN et al. PoseCNN: a convolutional neural network for 6D object pose estimation in cluttered scenes. arXiv preprint(2017).

    [22] J W GUO, X J XING, W Z QUAN et al. Efficient center voting for object detection and 6D pose estimation in 3D point cloud. IEEE Transactions on Image Processing, 30, 5072-5084(2021).

    [23] G Y ZHOU, H Q WANG, J X CHEN et al. PR-GCN: a deep graph convolutional network with point refinement for 6D pose estimation, 2773-2782(2021).

    [24] H Y LI, J H LIN, K JIA. DCL-Net Deep Correspondence Learning Network for 6D Pose Estimation. Lecture Notes in Computer Science, 369-385(2022).

    [25] G WANG, F MANHARDT, F TOMBARI et al. GDR-Net: geometry-guided direct regression network for monocular 6D object pose estimation, 16606-16616(2021).

    [26] Y L HU, M SALZMANN. Perspective flow aggregation for data-limited 6D object pose estimation. Lecture Notes in Computer Science, 89-106(2022).

    [27] Y Z SU, M SALEH, T FETZER et al. ZebraPose: coarse to fine surface encoding for 6DoF object pose estimation, 6728-6738(2022).

    [28] Y LABBÉ, J CARPENTIER, M AUBRY et al. CosyPose Consistent Multi-View Multi-Object 6D Pose Estimation. Computer Vision – ECCV 2020, 574-591(2020).

    [29] Y Z WU, A JAVAHERI, M ZAND et al. Keypoint cascade voting for point cloud based 6DoF pose estimation, 176-186(2022).

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    Minjia CHEN, Shaoyan GAI, Feipeng DA, Jian YU. Object 6-DoF pose estimation using auxiliary learning[J]. Optics and Precision Engineering, 2024, 32(6): 901

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    Paper Information

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    Received: Jul. 26, 2023

    Accepted: --

    Published Online: Apr. 19, 2024

    The Author Email: GAI Shaoyan (qxxymm@163.com), YU Jian (yujian@seu.edu.cn)

    DOI:10.37188/OPE.20243206.0901

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