{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T01:46:59Z","timestamp":1782265619671,"version":"3.54.5"},"reference-count":97,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pose estimation is an important task in the field of machine vision, being widely used in robot grasping, augmented reality, and other applications. Weakly textured objects pose severe challenges due to scarce texture and low-density features, becoming a bottleneck in robot grasping. This paper systematically reviews recent progress in weakly textured object pose estimation, classifying methods into traditional and deep learning categories, and further dividing deep learning methods into instance-level, category-level, and unseen object-level. This review further summarizes the core issues of generalization limitations, real-time contradictions, and data bottlenecks in existing research. Combined with the practical needs of weakly textured scenes, the review points out that multimodal fusion optimization, lightweight model design, and low-cost annotation technology development are the future core research directions. The research results can provide a reference for algorithm design, experimental verification, and engineering applications in the field of weakly textured object pose estimation.<\/jats:p>","DOI":"10.3390\/s26123957","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T01:03:23Z","timestamp":1782263003000},"page":"3957","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Weakly Textured Objects Pose Estimation: A Comprehensive Review"],"prefix":"10.3390","volume":"26","author":[{"given":"Jialun","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6779-9975","authenticated-orcid":false,"given":"Fanwu","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyang","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhao","family":"Shu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s00371-026-04383-4","article-title":"Enhanced feature fusion for precise small object detection in complex environments","volume":"42","author":"Li","year":"2026","journal-title":"Vis. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"146430","DOI":"10.1109\/ACCESS.2024.3473936","article-title":"Fundamental Coordinate Space for Object 6D Pose Estimation","volume":"12","author":"Wan","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","first-page":"2277","article-title":"6D pose estimation of binocular vision object based on 3D key point","volume":"59","author":"Ning","year":"2025","journal-title":"J. Zhejiang Univ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"457","DOI":"10.37188\/CJLCD.2024-0182","article-title":"Estimation method of category-level multi-object rigid body 6D pose","volume":"40","author":"Cheng","year":"2025","journal-title":"Chin. J. Liq. Cryst. Disp."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1016\/j.procir.2025.02.251","article-title":"Robotic Evaluation Framework for 6D Object Pose Estimation Accuracy","volume":"134","author":"Sebastian","year":"2025","journal-title":"Procedia CIRP"},{"key":"ref_6","first-page":"2504413","article-title":"EANet: Edge-Attention 6D Pose Estimation Network for Texture-Less Objects","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6728","DOI":"10.1109\/TCSVT.2022.3169144","article-title":"Category-Level 6D Object Pose Estimation with Structure Encoder and Reasoning Attention","volume":"32","author":"Liu","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/978-981-33-4676-5_8","article-title":"Copy\u2013Move Forgery Detection by Using Key-Point-Based Harris Features and CLA Clustering","volume":"216","author":"Rathi","year":"2021","journal-title":"Smart Innov. Syst. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4065","DOI":"10.1109\/TRO.2024.3433870","article-title":"Challenges for Monocular 6D Object Pose Estimation in Robotics","volume":"40","author":"Thalhammer","year":"2024","journal-title":"IEEE Trans. Robot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6816","DOI":"10.1109\/TCSVT.2025.3538018","article-title":"Learning Cross-View Consistent 3D Keypoints for Object 6D Pose Estimation","volume":"35","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_11","first-page":"25","article-title":"Review on Distinctive Image Features from Scale-Invariant Key-Points","volume":"8","author":"Sonali","year":"2018","journal-title":"Int. J. Soft Comput. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rusinkiewicz, S., and Levoy, M. (2001). Efficient variants of the ICP algorithm. Proceedings of 3rd International Conference on 3-D Imaging and Modeling (3DIM), IEEE.","DOI":"10.1109\/IM.2001.924423"},{"key":"ref_13","first-page":"40","article-title":"Reconstructing Rome","volume":"43","author":"Sameer","year":"2010","journal-title":"Computer"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Han, Z., and Liu, L. (2025). A 6D Object Pose Estimation Algorithm for Autonomous Docking with Improved Maximal Cliques. Sensors, 25.","DOI":"10.3390\/s25010283"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1109\/TPAMI.2021.3136301","article-title":"Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation","volume":"46","author":"Wang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1109\/LRA.2020.2967325","article-title":"Learning to assemble: Estimating 6D poses for robotic object-object manipulation","volume":"5","author":"Stefan","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/978-3-030-01231-1_42","article-title":"DeepIM: Deep iterative matching for 6D pose estimation","volume":"11210","author":"Li","year":"2018","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Usman, B., Tagliasacchi, A., Saenko, K., and Sud, A. (2002). MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.00664"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1007\/978-3-031-73226-3_5","article-title":"OP-Align: Object-Level and Part-Level Alignment for Self-supervised Category-Level Articulated Object Pose Estimation","volume":"15133","author":"Che","year":"2025","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1007\/978-3-319-10605-2_35","article-title":"Learning 6D Object Pose Estimation Using 3D Object Coordinates","volume":"8690","author":"Brachmann","year":"2014","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1007\/978-3-030-58548-8_38","article-title":"Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images","volume":"12349","author":"Park","year":"2020","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_22","first-page":"100651","article-title":"Shifted Hexpo activation function: An improved vanishing gradient mitigation activation function for disease classification","volume":"20","author":"Joseph","year":"2025","journal-title":"Mach. Learn. Appl."},{"key":"ref_23","first-page":"989","article-title":"Performance Comparison of Stereo Correspondence Algorithms in Dense Image Matching","volume":"28","author":"Ahmed","year":"2024","journal-title":"Comput. Y Sist."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lai, Y., Song, Y., Zhang, Q., Wang, Y., An, K., and Zhang, H. (2026). Pose-Perceptive Convolution: Learning Geometry-Adaptive Receptive Fields for Robust 6D Pose Estimation. Sensors, 26.","DOI":"10.3390\/s26020453"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"19224","DOI":"10.1109\/TITS.2022.3157128","article-title":"Compensating for Local Ambiguity With Encoder-Decoder in Urban Scene Segmentation","volume":"23","author":"Tang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bertels, M., Jutzi, B., and Ulrich, M. (2022). Automatic Real-Time Pose Estimation of Machinery from Images. Sensors, 22.","DOI":"10.3390\/s22072627"},{"key":"ref_27","unstructured":"Li, X., and Zhang, H. (2017). An Improved Canny Edge Detection Algorithm. IEEE 8th International Conference on Software Engineering and Service Science, IEEE."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. Proceedings of the IEEE International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"66","DOI":"10.23919\/JCC.2020.09.006","article-title":"An Algorithm Based on Markov Chain to Improve Edge Cache Hit Ratio for Blockchain-Enabled IoT","volume":"17","author":"Wang","year":"2020","journal-title":"China Commun."},{"key":"ref_30","unstructured":"Si, Z., Wu, X., and Liu, G. (2017, January 25\u201326). Monocular Visual Odometry Based on Homogeneous SURF Feature Points. Proceedings of the 5th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2017), Beijing, China."},{"key":"ref_31","unstructured":"Ouyang, X., Zhao, N., and Zhang, J. (2016, January 28\u201329). Improved NCC Stereo Matching Algorithm. Proceedings of the 2016 International Conference on Applied Mechanics, Electronics and Mechatronics Engineering (AMEME 2016), Beijing, China."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., and Navab, N. (2013). Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes. Computer Vision\u2014ACCV 2012. ACCV 2012, Springer.","DOI":"10.1007\/978-3-642-37331-2_42"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A Method for Registration of 3-D Shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Barath, D., Mishkin, D., Eichhardt, I., Shipachev, I., and Matas, J. (2021). Efficient initial pose-graph generation for global SfM. Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR46437.2021.01431"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kasyanov, A., Engelmann, F., Stuckler, J., and Leibe, B. (2017). Keyframe-Based Visual-Inertial Online SLAM with Relocalization. IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2017.8206581"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"132477","DOI":"10.1016\/j.optcom.2025.132477","article-title":"Plug-and-play algorithm based on hybrid priors for single-pixel imaging","volume":"596","author":"Chang","year":"2025","journal-title":"Opt. Commun."},{"key":"ref_37","first-page":"1","article-title":"Rotation-invariant 3D convolutional neural networks for 6D object pose estimation","volume":"28","author":"Chen","year":"2025","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7417","DOI":"10.1109\/TPAMI.2021.3118833","article-title":"DPODv2: Dense Correspondence-Based 6-DoF Pose Estimation","volume":"44","author":"Shugurov","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"513","DOI":"10.26599\/CVM.2025.9450404","article-title":"Sem-iNeRF: Camera Pose Refinement by Inverting Neural Radiance Fields with Semantic Feature Consistency","volume":"11","author":"Liu","year":"2025","journal-title":"Comput. Vis. Media"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TPAMI.2020.3047388","article-title":"PVNet: Pixel-Wise Voting Network for 6DoF Object Pose Estimation","volume":"44","author":"Peng","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Su, Y., Saleh, M., Fetzer, T., Rambach, J., Navab, N., Busam, B., Stricker, D., and Tombari, F. (2022). ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation. arXiv.","DOI":"10.1109\/CVPR52688.2022.00662"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","article-title":"DenseFuse: A Fusion Approach to Infrared and Visible Images","volume":"28","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e70049","DOI":"10.1002\/cav.70049","article-title":"A Real-Time Virtual-Real Fusion Rendering Framework in Cloud-Edge Environments","volume":"36","author":"Zhou","year":"2025","journal-title":"Comput. Animat. Virtual Worlds"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, Y., Sun, W., Huang, H., Liu, J., Fan, H., and Sun, J. (2020). PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.01165"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"127652","DOI":"10.1016\/j.neucom.2024.127652","article-title":"TransPose: 6D object pose estimation with geometry-aware Transformer","volume":"589","author":"Lin","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lin, J., Liu, L., Lu, D., and Jia, K. (2024). SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation. 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.02636"},{"key":"ref_47","unstructured":"Sarode, V., Li, X., Goforth, H., Aoki, Y., Srivatsan, R.A., Lucey, S., and Choset, H. (2019). PCRNet: Point Cloud Registration Network using PointNet Encoding. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, W., Duan, J., Basevi, H., Chang, H.J., and Leonardis, A. (2020). PointPoseNet: Point Pose Network for Robust 6D Object Pose Estimation. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE.","DOI":"10.1109\/WACV45572.2020.9093272"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/978-3-030-58558-7_43","article-title":"DeepGMR: Learning Latent Gaussian Mixture Models for Registration","volume":"12350","author":"Yuan","year":"2020","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cui, Z., Zhang, Q., and Liu, J. (2024). PS6D: Point Cloud Based Symmetry-Aware 6D Object Pose Estimation in Robot Bin-Picking. IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS58592.2024.10802046"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, H., Sridhar, S., Huang, J., Valentin, J., Song, S., and Guibas, L.J. (2019). Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation. IEEE Conference on Computer Vision and Pattern Recognition 2019, IEEE.","DOI":"10.1109\/CVPR.2019.00275"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zheng, L., Wang, C., Sun, Y., Dasgupta, E., Chen, H., Leonardis, A., Zhang, W., and Chang, H.J. (2023). HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52729.2023.01646"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Liu, J., Chen, Y., Ye, X., and Qi, X. (2023). IST-Net: Prior-free Category-level Pose Estimation with Implicit Space Transformation. IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51070.2023.01285"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wu, M., and Dong, H. (2023). GenPose: Generative Category-level Object Pose Estimation via Diffusion Models. arXiv.","DOI":"10.52202\/075280-2381"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"132056","DOI":"10.1016\/j.neucom.2025.132056","article-title":"ASD-pose: Adaptive sparse-to-dense point cloud reconstruction for category-level pose estimation","volume":"664","author":"Tang","year":"2026","journal-title":"Neurocomputing"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1109\/TCSVT.2024.3482439","article-title":"ZeroPose: CAD-Prompted Zero-Shot Object 6D Pose Estimation in Cluttered Scenes","volume":"35","author":"Chen","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chen, Y., Di, Y., Zhai, G., Manhardt, F., Zhang, C., Zhang, R., Tombari, F., Navab, N., and Busam, B. (2024). SecondPose: SE (3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00950"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/978-3-031-73347-5_10","article-title":"FoundPose: Unseen Object Pose Estimation with Foundation Features","volume":"15084","author":"Oernek","year":"2025","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1007\/978-3-031-73226-3_24","article-title":"FreeZe: Training-Free Zero-Shot 6D Pose Estimation with Geometric and Vision Foundation Models","volume":"15133","author":"Caraffa","year":"2025","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lee, T., Lee, B.-U., Shin, I., Choe, J., Shin, U., Kweon, I.S., and Yoon, K.-J. (2023). UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.01447"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Deng, W., Campbell, D., Sun, C., Zhang, J., Kanitkar, S., Shaffer, M.E., and Gould, S. (2025). Pos3R: 6D Pose Estimation for Unseen Objects Made Easy. 2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.01567"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s00138-025-01763-z","article-title":"CCPose: High-precision six-dimensional pose estimation for industrial objects","volume":"37","author":"Peter","year":"2026","journal-title":"Mach. Vis. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Liu, X., Wang, G., Zhang, R., Zhang, C., Tombari, F., and Ji, X. (2025). UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.02051"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Yue, Z., Han, Z., Yang, X., and Liu, L. (2024). DCSPose: A Dual-Channel Siamese Framework for Unseen Textureless Object Pose Estimation. Appl. Sci., 14.","DOI":"10.3390\/app14020730"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Moon, S., Son, H., Hur, D., and Kim, S. (2024). GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00957"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhao, C., Hu, Y., and Salzmann, M. (2024). LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation. International Conference on 3D Vision (3DV), IEEE.","DOI":"10.1109\/3DV62453.2024.00080"},{"key":"ref_68","unstructured":"Jiang, Z., Sinha, A., Cao, T., Ren, Y., Liu, B., and Xu, B. (2025). UnPose: Uncertainty-Guided Diffusion Priors for Zero-Shot Pose Estimation. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"11127","DOI":"10.1109\/TII.2024.3399886","article-title":"PoseDiffusion: A Coarse-to-Fine Framework for Unseen Object 6-DoF Pose Estimation","volume":"20","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3556","DOI":"10.1109\/TCSVT.2025.3613733","article-title":"Diff-COPE: Diffusion-Based Category-Level 6D Object Pose Estimation","volume":"36","author":"Tang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"8284","DOI":"10.1109\/LRA.2025.3585384","article-title":"HIPPo: Harnessing Image-to-3D Priors for Model-Free Zero-Shot 6D Pose Estimation","volume":"10","author":"Liu","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_72","unstructured":"Keunhong, P., Mousavian, A., Yu, X., and Fox, D. (2020). LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5770","DOI":"10.1109\/TMM.2025.3543083","article-title":"Geo6D: Geometric-Constraints-Guided Direct Object 6D Pose Estimation Network","volume":"27","author":"Chen","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Cai, D., Heikkil\u00e4, J., and Rahtu, E. (2025). GS-Pose: Generalizable Segmentation-Based 6D Object Pose Estimation with 3D Gaussian Splatting. International Conference on 3D Vision (3DV), IEEE.","DOI":"10.1109\/3DV66043.2025.00097"},{"key":"ref_75","unstructured":"Zhao, C., Zhang, T., Dang, Z., and Salzmann, M. (2024). DVMNet++: Rethinking Relative Pose Estimation for Unseen Objects. arXiv."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Cao, T., Luo, F., Qin, J., Jiang, Y., Wang, Y., and Xiao, C. (2025). iG-6DoF: Model-Free 6DoF Pose Estimation for Unseen Object via Iterative 3D Gaussian Splatting. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.00603"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.procir.2019.04.115","article-title":"An improved method for model-based training, detection and pose estimation of texture-less 3D objects in occlusion scenes","volume":"83","author":"Zou","year":"2019","journal-title":"Procedia CIRP"},{"key":"ref_78","first-page":"2072","article-title":"Research on Occluded Objects 6DoF Pose Estimation with Multi-feature and Pixel-level Fusion","volume":"14","author":"Liang","year":"2020","journal-title":"J. Front. Comput. Sci. Technol."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hodan, T., Haluza, P., Obdrzalek, S., Matas, J., Lourakis, M., and Zabulis, X. (2017). T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects. IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE.","DOI":"10.1109\/WACV.2017.103"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-91569-7_1","article-title":"YCB-Ev 1.1: Event-Vision Dataset for 6DoF Object Pose Estimation","volume":"15623","author":"Rojtberg","year":"2025","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_81","unstructured":"Bertram, D., Markus, U., Paul, B., Philipp, H., and Carsten, S. (2017). Introducing MVTec ITODD\u2014A Dataset for 3D Object Recognition in Industry. IEEE International Conference on Computer Vision Workshops, IEEE."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/LSP.2025.3631537","article-title":"Uncertainty-Guided Cross-Modal Distillation for Category-Level Object Pose Estimation","volume":"33","author":"Wang","year":"2025","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_83","unstructured":"Fu, Y., and Wang, X. (December, January 28). Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset. Proceedings of the 36th Conference on Neural Information Processing Systems, NeurIPS, Virtual."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1007\/978-3-031-72698-9_13","article-title":"Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation","volume":"15083","author":"Zhang","year":"2025","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Hodan, T., Sundermeyer, M., Labbe, Y., Nguyen, V.N., Wang, G., Brachmann, R., Drost, B., Lepetit, V., Rother, C., and Matas, J. (2024). BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects. IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE.","DOI":"10.1109\/CVPRW63382.2024.00570"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"10531","DOI":"10.3390\/app112210531","article-title":"A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects\u2019 6D Pose Estimation","volume":"11","author":"Wu","year":"2021","journal-title":"Appl. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1049\/cvi2.12113","article-title":"Gaussian guided IoU: A better metric for balanced learning on object detection","volume":"16","author":"Gou","year":"2022","journal-title":"IET Comput. Vision."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.1080\/01691864.2024.2416015","article-title":"The impact of hand parameter optimization on industrial bin picking","volume":"38","author":"Yukiyasu","year":"2024","journal-title":"Adv. Robot."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"4604","DOI":"10.1109\/LRA.2025.3553048","article-title":"Pose Estimation of Magnetically Driven Helical Robots with Eye-in-Hand Magnetic Sensing","volume":"10","author":"Zeng","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Li, S., Zhang, B., Zheng, J., Wang, D., and Liu, Z. (2024). Development of Automated 3D LiDAR System for Dimensional Quality Inspection of Prefabricated Concrete Elements. Sensors, 24.","DOI":"10.3390\/s24237486"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"5227","DOI":"10.1109\/TPAMI.2024.3362475","article-title":"SpectralGPT: Spectral Remote Sensing Foundation Model","volume":"46","author":"Hong","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Kerrin, E.W., Michaela, K., Quentin, L., and Mirko, M. (2023). Measuring teamwork for training in healthcare using eye tracking and pose estimation. Front. Psychol., 14.","DOI":"10.3389\/fpsyg.2023.1169940"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"104385","DOI":"10.1016\/j.inffus.2026.104385","article-title":"Im2State: Image-to-state Mamba for hyperspectral and multispectral image fusion","volume":"134","author":"Li","year":"2026","journal-title":"Inf. Fusion."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.ifacol.2025.11.158","article-title":"A Monocular Vision-Based Algorithm for Spacecraft Pose Estimation","volume":"59","author":"He","year":"2025","journal-title":"IFAC-PapersOnLine"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Zhu, T., Guo, X., Yan, H., Chen, Y., and Shu, B. (2022). Research on pose and position estimation based on mask RCNN edge extraction for AR auxiliary assembly system. SPIE\u2014The International Society for Optical Engineering, SPIE.","DOI":"10.1117\/12.2661820"},{"key":"ref_96","first-page":"923","article-title":"Embodied Spatial Navigation Training in Mild Cognitive Impairment: A Proof-of-Concept Trial","volume":"100","author":"Cosimo","year":"2024","journal-title":"J. Alzheimer\u2019s Dis. JAD"},{"key":"ref_97","first-page":"130","article-title":"An Object Detection and Pose Estimation Method for AR Application","volume":"1700","author":"Ji","year":"2022","journal-title":"Commun. Comput. Inf. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3957\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T01:11:36Z","timestamp":1782263496000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":97,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123957"],"URL":"https:\/\/doi.org\/10.3390\/s26123957","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,22]]}}}