6D Object Pose Estimation Enhanced by Structural Constraint

WANG Lichun; YANG Chao; FU Fangyu

doi:10.11936/bjutxb2023040019

Journal of Beijing University of Technology > 2025 > 51(2): 173-182. > DOI: 10.11936/bjutxb2023040019

WANG Lichun, YANG Chao, FU Fangyu. 6D Object Pose Estimation Enhanced by Structural Constraint[J]. Journal of Beijing University of Technology, 2025, 51(2): 173-182. DOI: 10.11936/bjutxb2023040019

Citation:

WANG Lichun, YANG Chao, FU Fangyu. 6D Object Pose Estimation Enhanced by Structural Constraint[J]. Journal of Beijing University of Technology, 2025, 51(2): 173-182. DOI: 10.11936/bjutxb2023040019

Citation:

WANG Lichun, YANG Chao, FU Fangyu. 6D Object Pose Estimation Enhanced by Structural Constraint[J]. Journal of Beijing University of Technology, 2025, 51(2): 173-182. DOI: 10.11936/bjutxb2023040019

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6D Object Pose Estimation Enhanced by Structural Constraint

WANG Lichun^{1, 2,},
YANG Chao^{1, 2},
FU Fangyu^{1, 2}

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2.
Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing 100124, China

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Received Date: April 10, 2023
Revised Date: May 21, 2023

Graphical Abstract

Abstract

Abstract

Aiming at the problem that the 6D object pose estimation method based on the voting strategy ignores the structural information between keypoints, a 6D object pose estimation method enhanced by structural constraint, SC-Pose, is proposed. This method defines a shape descriptor to describe the structural information between the 2D keypoints of the object. By increasing the keypoint structural loss to constrain the predicted shape descriptor to be close to the ground-truth shape descriptor, the positioning of the 2D keypoints is more accurate, thereby ultimately enhancing the accuracy of 6D object pose estimation. Results on the LINEMOD, OCC-LINEMOD and TruncationLINEMOD datasets show that SC-Pose can significantly boost the accuracy of 6D object pose estimation.
- 6D object pose estimation,
- unit vector-field,
- voting strategy,
- structural loss,
- grasping interaction,
- deep network

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References (28)

References

[1]	RAD M, LEPETIT V. BB8: a scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth[C]//2017 IEEE International Conference on Computer Vision. Piscataway, NJ: IEEE, 2017: 3848-3856.
[2]	TEKIN B, SINHA S N, FUA P. Real-time seamless single shot 6D object pose prediction[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2018: 292-301.
[3]	LEPETIT V, MORENO-NOGUER F, FUA P. EPnP: an accurate O(n) solution to the PnP problem[J]. International Journal of Computer Vision, 2009, 81(2): 155-166.
[4]	PENG S D, LIU Y, HUANG Q X, et al. PVNet: pixel-wise voting network for 6DoF pose estimation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2019: 4561-4570.
[5]	SONG C, SONG J R, HUANG Q X. HybridPose: 6D object pose estimation under hybrid representations[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2020: 431-440.
[6]	XIONG F, LIU C J, CHEN Q J. Region pixel voting network (RPVNet) for 6D pose estimation from monocular image[J]. Applied Sciences, 2021, 11(2): 743.
[7]	LIU X Y, IWASE S, KITANI K M. KDFNet: learning keypoint distance field for 6D object pose estimation[C]//2021 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ: IEEE, 2021: 4631-4638.
[8]	YU X, ZHUANG Z Y, KONIUSZ P, et al. 6DoF object pose estimation via differentiable proxy voting regularizer[C]//British Machine Vision Conference. Berlin: Springer, 2020: 1-12.
[9]	ZAKHAROV S, SHUGUROV I, ILIC S. DPOD: 6D pose object detector and refiner[C]//2019 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2019: 1941-1950.
[10]	PARK K, PATTEN T, VINCZE M. Pix2Pose: pixel-wise coordinate regression of objects for 6D pose estimation[C]//2019 IEEE/CVF International Conference on Computer Vision. Piscataway, NJ: IEEE, 2019: 7668-7677.
[11]	LIU X Y, JONSCHKOWSKI R, ANGELOVA A, et al. KeyPose: multi-view 3D labeling and keypoint estimation for transparent objects[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2020: 11602-11610.
[12]	HODAN T, BARATH D, MATAS J. EPOS: estimating 6D pose of objects with symmetries[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2020: 11703-11712.
[13]	XIANG Y, SCHMIDT T, NARAYANAN V, et al. PoseCNN: a convolutional neural network for 6D object pose estimation in cluttered scenes[EB/OL]. [2023-05-20]. http://export.arxiv.org/abs/1711.00199.
[14]	KEHL W, MANHARDT F, TOMBARI F, et al. SSD-6D: making RGB-based 3D detection and 6D pose estimation great again[C]//2017 IEEE International Conference on Computer Vision. Piscataway, NJ: IEEE, 2017: 1521-1529.
[15]	DO T T, CAI M, PHAM T, et al. Deep-6DPose: recovering 6D object pose from a single RGB image[EB/OL]. [2023-03-20]. https://arxiv.org/abs/1802.10367.
[16]	LI Z G, WANG G, JI X Y. CDPN: coordinates-based disentangled pose network for real-time RGB-based 6-DoF object pose estimation[C]//2019 IEEE/CVF International Conference on Computer Vision. Piscataway, NJ: IEEE, 2019: 7678-7687.
[17]	TIAN M, PAN L, ANG M H, et al. Robust 6D object pose estimation by learning RGB-D features[C]//2020 IEEE International Conference on Robotics and Automation. Piscataway, NJ: IEEE, 2020: 6218-6224.
[18]	WANG H, SRIDHAR S, HUANG J W, et al. Normalized object coordinate space for category-level 6D object pose and size estimation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2019: 2642-2651.
[19]	TIAN M, ANG M H Jr, LEE G H. Shape prior deformation for categorical 6D object pose and size estimation[C]//European Conference on Computer Vision. Cham: Springer, 2020: 530-546.
[20]	PARK K, MOUSAVIAN A, XIANG Y, et al. LatentFusion: end-to-end differentiable reconstruction and rendering for unseen object pose estimation[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2020: 10710-10719.
[21]	CHEN D S, LI J, WANG Z, et al. Learning canonical shape space for category-level 6D object pose and size estimation[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2020: 11973-11982.
[22]	AMINI A, PERIYASAMY A S, BEHNKE S. T6D-Direct: transformers for multi-object 6D pose direct regression[C]//DAGM German Conference on Pattern Recognition. Cham: Springer, 2021: 530-544.
[23]	BUKSCHAT Y, VETTER M. EfficientPose: an efficient, accurate and scalable end-to-end 6D multi object pose estimation approach[EB/OL]. [2023-03-20]. https://arxiv.org/abs/2011.04307.
[24]	SHEN W, ZHAO K, JIANG Y, et al. Object skeleton extraction in natural images by fusing scale-associated deep side outputs[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2016: 222-230.
[25]	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2016: 770-778.
[26]	HINTERSTOISSER S, LEPETIT V, ILIC S, et al. Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes[C]//Asian Conference on Computer Vision. Berlin: Springer, 2012: 548-562.
[27]	BRACHMANN E, KRULL A, MICHEL F, et al. Learning 6D object pose estimation using 3D object coordinates[C]//European Conference on Computer Vision. Cham: Springer, 2014: 536-551.
[28]	XIAO J X, HAYS J, EHINGER K A, et al. SUN database: large-scale scene recognition from abbey to zoo[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2010: 3485-3492.

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6D Object Pose Estimation Enhanced by Structural Constraint