{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:18:24Z","timestamp":1777655904984,"version":"3.51.4"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200854","type":"print"},{"value":"9783031200861","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20086-1_16","type":"book-chapter","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T10:31:55Z","timestamp":1668076315000},"page":"275-292","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["ShAPO: Implicit Representations for\u00a0Multi-object Shape, Appearance, and\u00a0Pose Optimization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1955-6194","authenticated-orcid":false,"given":"Muhammad Zubair","family":"Irshad","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6231-6137","authenticated-orcid":false,"given":"Sergey","family":"Zakharov","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3111-3812","authenticated-orcid":false,"given":"Rares","family":"Ambrus","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2598-8118","authenticated-orcid":false,"given":"Thomas","family":"Kollar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2626-2004","authenticated-orcid":false,"given":"Zsolt","family":"Kira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8820-550X","authenticated-orcid":false,"given":"Adrien","family":"Gaidon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"16_CR1","unstructured":"Chang, A.X., et al.: ShapeNet: an information-rich 3d model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Chen, D., Li, J., Wang, Z., Xu, K.: Learning canonical shape space for category-level 6d object pose and size estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11973\u201311982 (2020)","DOI":"10.1109\/CVPR42600.2020.01199"},{"key":"16_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-3-030-58574-7_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Chen","year":"2020","unstructured":"Chen, X., Dong, Z., Song, J., Geiger, A., Hilliges, O.: Category level object pose estimation via neural analysis-by-synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12371, pp. 139\u2013156. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58574-7_9"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5939\u20135948 (2019)","DOI":"10.1109\/CVPR.2019.00609"},{"key":"16_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/978-3-319-46484-8_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"CB Choy","year":"2016","unstructured":"Choy, C.B., Xu, D., Gwak, J.Y., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628\u2013644. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_38"},{"issue":"2","key":"16_CR6","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/LRA.2016.2645124","volume":"2","author":"CG Cifuentes","year":"2016","unstructured":"Cifuentes, C.G., Issac, J., W\u00fcthrich, M., Schaal, S., Bohg, J.: Probabilistic articulated real-time tracking for robot manipulation. IEEE Robot. Autom. Lett. 2(2), 577\u2013584 (2016)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: CenterNet: keypoint triplets for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6569\u20136578 (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3d object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 605\u2013613 (2017)","DOI":"10.1109\/CVPR.2017.264"},{"key":"16_CR9","unstructured":"Ferrari, C., Canny, J.F.: Planning Optimal Grasps. In: ICRA, vol. 3, p. 6 (1992)"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Gkioxari, G., Malik, J., Johnson, J.: Mesh R-CNN. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9785\u20139795 (2019)","DOI":"10.1109\/ICCV.2019.00988"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Goodwin, W., Vaze, S., Havoutis, I., Posner, I.: Zero-shot category-level object pose estimation. arXiv preprint (2022)","DOI":"10.1007\/978-3-031-19842-7_30"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B., Aubry, M.: AtlasNet: A Papier-M\u00e2ch\u00e9 Approach to Learning 3D Surface Generation. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00030"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Hodan, T., Barath, D., Matas, J.: Epos: estimating 6d pose of objects with symmetries. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01172"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Irshad, M.Z., Kollar, T., Laskey, M., Stone, K., Kira, Z.: Centersnap: single-shot multi-object 3d shape reconstruction and categorical 6d pose and size estimation. In: IEEE International Conference on Robotics and Automation (ICRA) (2022). https:\/\/arxiv.org\/abs\/2203.01929","DOI":"10.1109\/ICRA46639.2022.9811799"},{"key":"16_CR17","doi-asserted-by":"publisher","unstructured":"Irshad, M.Z., Ma, C.Y., Kira, Z.: Hierarchical cross-modal agent for robotics vision-and-language navigation. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 13238\u201313246 (2021). https:\/\/doi.org\/10.1109\/ICRA48506.2021.9561806","DOI":"10.1109\/ICRA48506.2021.9561806"},{"key":"16_CR18","unstructured":"Irshad, M.Z., Mithun, N.C., Seymour, Z., Chiu, H.P., Samarasekera, S., Kumar, R.: Sasra: semantically-aware spatio-temporal reasoning agent for vision-and-language navigation in continuous environments (2022). https:\/\/arxiv.org\/abs\/2108.11945"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Zhu, Y., Svetlik, M., Fang, K., Zhu, Y.: Synergies between affordance and geometry: 6-Dof grasp detection via implicit representations. Robotics: science and systems (2021)","DOI":"10.15607\/RSS.2021.XVII.024"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Kato, H., Ushiku, Y., Harada, T.: Neural 3d mesh renderer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3907\u20133916 (2018)","DOI":"10.1109\/CVPR.2018.00411"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Kehl, W., Manhardt, F., Tombari, F., Ilic, S., Navab, N.: Ssd-6d: making rgb-based 3d detection and 6d pose estimation great again. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.169"},{"key":"16_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/978-3-319-46487-9_13","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Kehl","year":"2016","unstructured":"Kehl, W., Milletari, F., Tombari, F., Ilic, S., Navab, N.: Deep learning of local RGB-D patches for 3D\u00a0object detection and 6D pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 205\u2013220. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_13"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Girshick, R., He, K., Doll\u00e1r, P.: Panoptic feature pyramid networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6399\u20136408 (2019)","DOI":"10.1109\/CVPR.2019.00656"},{"key":"16_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/978-3-030-58580-8_16","volume-title":"Computer Vision \u2013 ECCV 2020","author":"W Kuo","year":"2020","unstructured":"Kuo, W., Angelova, A., Lin, T.-Y., Dai, A.: Mask2CAD: 3D shape prediction by learning to segment and retrieve. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 260\u2013277. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_16"},{"key":"16_CR25","unstructured":"Laskey, M., Thananjeyan, B., Stone, K., Kollar, T., Tjersland, M.: SimNet: enabling robust unknown object manipulation from pure synthetic data via stereo. In: 5th Annual Conference on Robot Learning (2021)"},{"issue":"4","key":"16_CR26","doi-asserted-by":"publisher","first-page":"8575","DOI":"10.1109\/LRA.2021.3110538","volume":"6","author":"T Lee","year":"2021","unstructured":"Lee, T., Lee, B.U., Kim, M., Kweon, I.S.: Category-level metric scale object shape and pose estimation. IEEE Robot. Autom. Lett. 6(4), 8575\u20138582 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Li, Z., Niklaus, S., Snavely, N., Wang, O.: Neural scene flow fields for space-time view synthesis of dynamic scenes. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00643"},{"key":"16_CR28","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3d reconstruction in function space. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"key":"16_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/978-3-030-58452-8_24","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Mildenhall","year":"2020","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405\u2013421. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_24"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J., Zhang, J.J.: Total3dunderstanding: joint layout, object pose and mesh reconstruction for indoor scenes from a single image. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020","DOI":"10.1109\/CVPR42600.2020.00013"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Geiger, A.: Giraffe: Representing scenes as compositional generative neural feature fields. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01129"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: learning implicit 3d representations without 3d supervision. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00356"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., Geiger, A.: Texture fields: learning texture representations in function space. In: Proceedings IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00463"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Ost, J., Mannan, F., Thuerey, N., Knodt, J., Heide, F.: Neural scene graphs for dynamic scenes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2856\u20132865, June 2021","DOI":"10.1109\/CVPR46437.2021.00288"},{"key":"16_CR36","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: learning continuous signed distance functions for shape representation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Park, K., Patten, T., Vincze, M.: Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00776"},{"key":"16_CR38","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024\u20138035. Curran Associates, Inc. (2019)"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: Pvnet: pixel-wise voting network for 6dof pose estimation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00469"},{"key":"16_CR40","doi-asserted-by":"crossref","unstructured":"Pitteri, G., Ramamonjisoa, M., Ilic, S., Lepetit, V.: On object symmetries and 6d pose estimation from images. In: 2019 International Conference on 3D Vision (3DV), pp. 614\u2013622. IEEE (2019)","DOI":"10.1109\/3DV.2019.00073"},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Rad, M., Lepetit, V.: Bb8: a scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.413"},{"key":"16_CR42","unstructured":"Remelli, E., Lukoianov, A., Richter, S., Guillard, B., Bagautdinov, T., Baque, P., Fua, P.: Meshsdf: differentiable iso-surface extraction. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 22468\u201322478. Curran Associates, Inc. (2020), https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/fe40fb944ee700392ed51bfe84dd4e3d-Paper.pdf"},{"key":"16_CR43","doi-asserted-by":"crossref","unstructured":"Shugurov, I., Zakharov, S., Ilic, S.: Dpodv2: Dense correspondence-based 6 dof pose estimation. TPAMI (2021)","DOI":"10.1109\/TPAMI.2021.3118833"},{"key":"16_CR44","unstructured":"Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. NeurIPS (2020)"},{"key":"16_CR45","unstructured":"Sitzmann, V., Zollhoefer, M., Wetzstein, G.: Scene representation networks: Continuous 3d-structure-aware neural scene representations. NeurIPS (2019)"},{"key":"16_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1007\/978-3-030-01231-1_43","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Sundermeyer","year":"2018","unstructured":"Sundermeyer, M., Marton, Z.-C., Durner, M., Brucker, M., Triebel, R.: Implicit 3D orientation learning for 6D object detection from RGB images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 712\u2013729. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_43"},{"issue":"3","key":"16_CR47","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1007\/s11263-019-01243-8","volume":"128","author":"M Sundermeyer","year":"2020","unstructured":"Sundermeyer, M., Marton, Z.C., Durner, M., Triebel, R.: Augmented autoencoders: implicit 3D orientation learning for 6d object detection. Int. J. Comput. Vision 128(3), 714\u2013729 (2020)","journal-title":"Int. J. Comput. Vision"},{"key":"16_CR48","doi-asserted-by":"crossref","unstructured":"Takikawa, T., et al.: Neural geometric level of detail: Real-time rendering with implicit 3d shapes. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01120"},{"key":"16_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1007\/978-3-319-10599-4_30","volume-title":"Computer Vision \u2013 ECCV 2014","author":"A Tejani","year":"2014","unstructured":"Tejani, A., Tang, D., Kouskouridas, R., Kim, T.-K.: Latent-class hough forests for 3D object detection and pose estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 462\u2013477. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_30"},{"key":"16_CR50","doi-asserted-by":"crossref","unstructured":"Tekin, B., Sinha, S.N., Fua, P.: Real-time seamless single shot 6d object pose prediction. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00038"},{"key":"16_CR51","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1007\/978-3-030-58589-1_32","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Tian","year":"2020","unstructured":"Tian, M., Ang, M.H., Lee, G.H.: Shape prior deformation for categorical 6D object pose and size estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 530\u2013546. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58589-1_32"},{"key":"16_CR52","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: 6-pack: category-level 6d pose tracker with anchor-based keypoints. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 10059\u201310066. IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9196679"},{"key":"16_CR53","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: Densefusion: 6d object pose estimation by iterative dense fusion. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00346"},{"key":"16_CR54","doi-asserted-by":"crossref","unstructured":"Wang, H., Sridhar, S., Huang, J., Valentin, J., Song, S., Guibas, L.J.: Normalized object coordinate space for category-level 6d object pose and size estimation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00275"},{"key":"16_CR55","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-030-01252-6_4","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Wang","year":"2018","unstructured":"Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., Jiang, Y.-G.: Pixel2Mesh: generating 3D mesh models from single RGB images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 55\u201371. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_4"},{"key":"16_CR56","doi-asserted-by":"crossref","unstructured":"Wen, B., Bekris, K.E.: Bundletrack: 6d pose tracking for novel objects without instance or category-level 3d models. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (2021)","DOI":"10.1109\/IROS51168.2021.9635991"},{"key":"16_CR57","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: Posecnn: a convolutional neural network for 6d object pose estimation in cluttered scenes. In: RSS (2018)","DOI":"10.15607\/RSS.2018.XIV.019"},{"key":"16_CR58","doi-asserted-by":"crossref","unstructured":"Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.: PCN: Point completion network. In: 3D Vision (3DV), 2018 International Conference on (2018)","DOI":"10.1109\/3DV.2018.00088"},{"key":"16_CR59","doi-asserted-by":"crossref","unstructured":"Zakharov, S., Kehl, W., Bhargava, A., Gaidon, A.: Autolabeling 3d objects with differentiable rendering of sdf shape priors. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01224"},{"key":"16_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cui, Z., Zhang, Y., Zeng, B., Pollefeys, M., Liu, S.: Holistic 3d scene understanding from a single image with implicit representation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00872"},{"key":"16_CR61","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)"},{"key":"16_CR62","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: On the continuity of rotation representations in neural networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00589"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20086-1_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T10:56:20Z","timestamp":1668077780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20086-1_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200854","9783031200861"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20086-1_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}