{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:11:48Z","timestamp":1777594308061,"version":"3.51.4"},"reference-count":64,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China under Grants","award":["61876170"],"award-info":[{"award-number":["61876170"]}]},{"name":"National Natural Science Fund Youth Science Fund of China under Grant","award":["51805168"],"award-info":[{"award-number":["51805168"]}]},{"name":"R&amp;D project of CRRC Zhuzhou Locomotive Co., LTD.","award":["2018GY121"],"award-info":[{"award-number":["2018GY121"]}]},{"name":"Fundamental Research Funds for Central Universities, China University of Geosciences","award":["CUG170692"],"award-info":[{"award-number":["CUG170692"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>6DoF object pose estimation is a foundation for many important applications, such as robotic grasping, automatic driving, and so on. However, it is very challenging to estimate 6DoF pose of transparent object which is commonly seen in our daily life, because the optical characteristics of transparent material lead to significant depth error which results in false estimation. To solve this problem, a two-stage approach is proposed to estimate 6DoF pose of transparent object from a single RGB-D image. In the first stage, the influence of the depth error is eliminated by transparent segmentation, surface normal recovering, and RANSAC plane estimation. In the second stage, an extended point-cloud representation is presented to accurately and efficiently estimate object pose. As far as we know, it is the first deep learning based approach which focuses on 6DoF pose estimation of transparent objects from a single RGB-D image. Experimental results show that the proposed approach can effectively estimate 6DoF pose of transparent object, and it out-performs the state-of-the-art baselines by a large margin.<\/jats:p>","DOI":"10.3390\/s20236790","type":"journal-article","created":{"date-parts":[[2020,11,28]],"date-time":"2020-11-28T03:51:16Z","timestamp":1606535476000},"page":"6790","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["6DoF Pose Estimation of Transparent Object from a Single RGB-D Image"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5301-9376","authenticated-orcid":false,"given":"Chi","family":"Xu","sequence":"first","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9357-565X","authenticated-orcid":false,"given":"Jiale","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0512-8180","authenticated-orcid":false,"given":"Mengyang","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7416-775X","authenticated-orcid":false,"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3721-4438","authenticated-orcid":false,"given":"Lijun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7980-9755","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Electric Locomotive Co., Ltd. 1 TianXin Road; Zhuzhou 412000, China"},{"name":"National Innovation Center of Advanced Rail Transit Equipment, Zhuzhou 412000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1109\/TPAMI.2012.41","article-title":"A Robust O(n) Solution to the Perspective-n-Point Problem","volume":"34","author":"Li","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, C., Xu, D., Zhu, Y., Mart\u00edn-Mart\u00edn, R., Lu, C., Fei-Fei, L., and Savarese, S. (2019, January 16\u201318). DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00346"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tian, M., Pan, L., Ang Jr, M.H., and Lee, G.H. (June, January 31). Robust 6D Object Pose Estimation by Learning RGB-D Features. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197555"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhu, M., Derpanis, K.G., Yang, Y., Brahmbhatt, S., Zhang, M., Phillips, C., Lecce, M., and Daniilidis, K. (2014, January 20\u201321). Single image 3D object detection and pose estimation for grasping. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Miami, Florida, USA.","DOI":"10.1109\/ICRA.2014.6907430"},{"key":"ref_5","unstructured":"Tremblay, J., To, T., Sundaralingam, B., Xiang, Y., Fox, D., and Birchfield, S. (2018). Deep object pose estimation for semantic robotic grasping of household objects. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The KITTI vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., and Xia, T. (2017, January 22\u201325). Multi-view 3D Object Detection Network for Autonomous Driving. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1109\/TSMCB.2005.850164","article-title":"Pose Estimation for Augmented Reality Applications Using Genetic Algorithm","volume":"35","author":"Yu","year":"2005","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1109\/TVCG.2015.2513408","article-title":"Pose Estimation for Augmented Reality: A Hands-On Survey","volume":"22","author":"Marchand","year":"2016","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kehl, W., Milletari, F., Tombari, F., Ilic, S., and Navab, N. (2016, January 8\u201316). Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation. Proceedings of the 2016 European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_13"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, C., Bai, J., and Hager, G.D. (2018, January 8\u201314). A Unified Framework for Multi-View Multi-Class Object Pose Estimation. Proceedings of the 2018 European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01270-0_16"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sajjan, S., Moore, M., Pan, M., Nagaraja, G., Lee, J., Zeng, A., and Song, S. (June, January 31). Clear Grasp: 3D Shape Estimation of Transparent Objects for Manipulation. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197518"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Peng, S., Liu, Y., Huang, Q., Zhou, X., and Bao, H. (2019, January 16\u201318). PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00469"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Drost, B., Ulrich, M., Navab, N., and Ilic, S. (2010, January 13\u201318). Model globally, match locally: Efficient and robust 3D object recognition. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, SanFrancisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540108"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Vidal, J., Lin, C., and Mart\u00ed, R. (2018, January 20\u201323). 6D pose estimation using an improved method based on point pair features. Proceedings of the 2018 4th International Conference on Control, Automation and Robotics (ICCAR), Auckland, New Zealand.","DOI":"10.1109\/ICCAR.2018.8384709"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., Holzer, S., Cagniart, C., Ilic, S., Konolige, K., Navab, N., and Lepetit, V. (2011, January 6\u201313). Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126326"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s11263-015-0824-y","article-title":"A Comprehensive Performance Evaluation of 3D Local Feature Descriptors","volume":"116","author":"Guo","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Song, S., and Xiao, J. (2016, January 27\u201330). Deep sliding shapes for amodal 3d object detection in rgb-d images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.94"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Park, K., Mousavian, A., Xiang, Y., and Fox, D. (2020, January 14\u201319). LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01072"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wada, K., Sucar, E., James, S., Lenton, D., and Davison, A.J. (2020, January 14\u201319). MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01455"},{"key":"ref_21","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 22\u201325). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_22","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017, January 4\u20139). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_23","first-page":"558","article-title":"An additive latent feature model for transparent object recognition","volume":"22","author":"Fritz","year":"2009","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","unstructured":"Mchenry, K., Ponce, J., and Forsyth, D. (2005, January 20\u201326). Finding glass. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Phillips, C.J., Derpanis, K.G., and Daniilidis, K. (2011, January 6\u201313). A novel stereoscopic cue for figure-ground segregation of semi-transparent objects. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130373"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xie, E., Wang, W., Wang, W., Ding, M., Shen, C., and Luo, P. (2020). Segmenting Transparent Objects in the Wild. arXiv.","DOI":"10.24963\/ijcai.2021\/165"},{"key":"ref_27","unstructured":"Mchenry, K., and Ponce, J. (2006, January 17\u201322). A Geodesic Active Contour Framework for Finding Glass. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, USA."},{"key":"ref_28","unstructured":"Wang, T., He, X., and Barnes, N. (2012, January 11\u201315). Glass object localization by joint inference of boundary and depth. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Khaing, M.P., and Masayuki, M. (2018, January 14\u201315). Transparent object detection using convolutional neural network. Proceedings of the International Conference on Big Data Analysis and Deep Learning Applications, Miyazaki, Japan.","DOI":"10.1007\/978-981-13-0869-7_10"},{"key":"ref_30","unstructured":"Lai, P.J., and Fuh, C.S. (2015, January 17\u201319). Transparent object detection using regions with convolutional neural network. Proceedings of the IPPR Conference on Computer Vision, Graphics, and Image Processing, Taiwan, China."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bernstein, A.V., Olaru, A., and Zhou, J. (2016, January 14\u201316). Friend or foe: Exploiting sensor failures for transparent object localization and classification. Proceedings of the 2016 International Conference on Robotics and Machine Vision, Moscow, Russia.","DOI":"10.1117\/12.2266255"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Han, K., Wong, K.Y.K., and Liu, M. (2015, January 7\u201312). A Fixed Viewpoint Approach for Dense Reconstruction of Transparent Objects. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299026"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Qian, Y., Gong, M., and Yang, Y. (2016, January 27\u201330). 3D Reconstruction of Transparent Objects with Position-Normal Consistency. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.473"},{"key":"ref_34","unstructured":"Jawahar, C., Li, H., Mori, G., and Schindler, K. (2019). Depth Reconstruction of Translucent Objects from a Single Time-of-Flight Camera Using Deep Residual Networks. Computer Vision\u2013ACCV 2018, Springer International Publishing."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Klank, U., Carton, D., and Beetz, M. (2011, January 9\u201313). Transparent object detection and reconstruction on a mobile platform. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5979793"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11457","DOI":"10.1364\/OE.17.011457","article-title":"Scanning from heating: 3D shape estimation of transparent objects from local surface heating","volume":"17","author":"Eren","year":"2009","journal-title":"Opt. Express"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"9796127","DOI":"10.1155\/2017\/9796127","article-title":"Fusing depth and silhouette for scanning transparent object with RGB-D sensor","volume":"2017","author":"Ji","year":"2017","journal-title":"Int. J. Opt."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, Z., Yeh, Y.Y., and Chandraker, M. (2020, January 13\u201319). Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00134"},{"key":"ref_39","unstructured":"Albrecht, S., and Marsland, S. (2013, January 24\u201328). Seeing the unseen: Simple reconstruction of transparent objects from point cloud data. Proceedings of the Robotics: Science and Systems, Berlin, Germany."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"273","DOI":"10.7551\/mitpress\/9816.003.0040","article-title":"Recognition and pose estimation of rigid transparent objects with a kinect sensor","volume":"273","author":"Lysenkov","year":"2013","journal-title":"Robotics"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lysenkov, I., and Rabaud, V. (2013, January 6\u201310). Pose estimation of rigid transparent objects in transparent clutter. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630571"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"012011","DOI":"10.1088\/1742-6596\/1183\/1\/012011","article-title":"Transparent object detection and location based on RGB-D camera","volume":"1183","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_43","unstructured":"Byambaa, M., Koutaki, G., and Choimaa, L. (2019, January 5\u20138). 6D Pose Estimation of Transparent Object from Single RGB Image. Proceedings of the Conference of Open Innovations Association, FRUCT, Helsinki, Finland."},{"key":"ref_44","unstructured":"Phillips, C.J., Lecce, M., and Daniilidis, K. (2016, January 18\u201322). Seeing Glassware: From Edge Detection to Pose Estimation and Shape Recovery. Proceedings of the Robotics: Science and Systems, Ann Arbor, MI, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, X., Jonschkowski, R., Angelova, A., and Konolige, K. (2020, January 13\u201319). KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01162"},{"key":"ref_46","unstructured":"Gavrilova, M.L., Tan, C.J.K., and Konushin, A. (2013). Pose Refinement of Transparent Rigid Objects with a Stereo Camera. Transactions on Computational Science XIX, Springer."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Pan, T., Wu, S., Chang, H., and Jenkins, O.C. (2019, January 3\u20138). GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967685"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Mathai, A., Guo, N., Liu, D., and Wang, X. (2020). 3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging. Sensors, 20.","DOI":"10.3390\/s20154211"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/LRA.2019.2942272","article-title":"Three-Dimensional Pose Estimation of Optically Transparent Microrobots","volume":"5","author":"Grammatikopoulou","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_50","unstructured":"Kaiming, H., Georgia, G., Piotr, D., and Ross, G. (2017, January 21\u201326). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Honolulu, HI, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 22\u201325). RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1111\/j.1467-8659.2007.01016.x","article-title":"Efficient RANSAC for point-cloud shape detection","volume":"Volume 26","author":"Schnabel","year":"2007","journal-title":"Computer Graphics Forum"},{"key":"ref_55","first-page":"1","article-title":"Dynamic Graph CNN for Learning on Point Clouds","volume":"38","author":"Wang","year":"2018","journal-title":"ACM Trans. Graph."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Schmidt, T., Narayanan, V., and Fox, D. (2017). PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes. arXiv.","DOI":"10.15607\/RSS.2018.XIV.019"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Brachmann, E., Michel, F., Krull, A., Yang, M.Y., Gumhold, S., and Rother, C. (2016, January 27\u201330). Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.366"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Rad, M., and Lepetit, V. (2017, January 22\u201329). BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.413"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tekin, B., Sinha, S.N., and Fua, P. (2017, January 21\u201326). Real-Time Seamless Single Shot 6D Object Pose Prediction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2018.00038"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kehl, W., Manhardt, F., Tombari, F., Ilic, S., and Navab, N. (2017, January 22\u201329). SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.169"},{"key":"ref_61","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 4\u20139). Automatic differentiation in pytorch. Proceedings of the 2017 Neural Information Processing Systems Workshop, Long Beach, CA, USA."},{"key":"ref_62","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., and Navab, N. (2012, January 5\u20139). Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes. Proceedings of the Asian Conference on Computer Vision, Daejeon, Korea.","DOI":"10.1007\/978-3-642-33885-4_60"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., and Navab, N. (2016, January 25\u201328). Deeper depth prediction with fully convolutional residual networks. Proceedings of the 2016 Fourth international conference on 3D vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.32"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6790\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:38:35Z","timestamp":1760179115000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6790"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,27]]},"references-count":64,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20236790"],"URL":"https:\/\/doi.org\/10.3390\/s20236790","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,27]]}}}