Fetching the paper…
Reading the bibliography…
In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D.
Hinterstoisser, S., Cagniart, C., Ilic, S., Sturm, P., Navab, N., Fua, P., Lepetit, V.: Gradient response maps for real-time detection of textureless objects. T-PAMI 34
2011
Earlier work this paper cites.
Hinterstoisser, S., Holzer, S., Cagniart, C., Ilic, S., Konolige, K., Navab, N., Lepetit, V.: Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: ICCV (2011)
2011
Earlier work this paper cites.
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In: ACCV (2012)
2012
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: ECCV (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Wohlhart, P., Lepetit, V.: Learning descriptors for object recognition and 3d pose estimation. In: CVPR (2015)
2015
Earlier work this paper cites.
Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: CVPR (2016)
2016
Earlier work this paper cites.
Balntas, V., Doumanoglou, A., Sahin, C., Sock, J., Kouskouridas, R., Kim, T.K.: Pose guided rgbd feature learning for 3d object pose estimation. In: ICCV (2017)
2017
Earlier work this paper cites.
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: CVPR (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. NeurIPS (2017)
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Hodan, T., Michel, F., Brachmann, E., Kehl, W., GlentBuch, A., Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., et al.: Bop: Benchmark for 6d object pose estimation. In: ECCV (2018)
2018
Earlier work this paper cites.
Li, Y., Wang, G., Ji, X., Xiang, Y., Fox, D.: Deepim: Deep iterative matching for 6d pose estimation. In: ECCV (2018)
2018
Earlier work this paper cites.
Sundermeyer, M., Marton, Z.C., Durner, M., Brucker, M., Triebel, R.: Implicit 3d orientation learning for 6d object detection from rgb images. In: ECCV (2018)
2018
Earlier work this paper cites.
Tekin, B., Sinha, S.N., Fua, P.: Real-time seamless single shot 6d object pose prediction. In: CVPR (2018)
2018
Earlier work this paper cites.
Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes. Robotics: Science and Systems (2018)
2018
Earlier work this paper cites.
Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: Pvnet: Pixel-wise voting network for 6-dof pose estimation. In: CVPR (2019)
2019
Earlier work this paper cites.
Pitteri, G., Ilic, S., Lepetit, V.: Cornet: generic 3d corners for 6d pose estimation of new objects without retraining. In: ICCVW (2019)
2019
Earlier work this paper cites.
Pitteri, G., Ramamonjisoa, M., Ilic, S., Lepetit, V.: On object symmetries and 6d pose estimation from images. In: 3DV (2019)
2019
Earlier work this paper cites.
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)
2019
Earlier work this paper cites.
Xiao, Y., Qiu, X., Langlois, P.A., Aubry, M., Marlet, R.: Pose from shape: Deep pose estimation for arbitrary 3d objects. In: BMVC (2019)
2019
Earlier work this paper cites.
Zakharov, S., Shugurov, I., Ilic, S.: Dpod: 6d pose object detector and refiner. In: ICCV (2019)
2019
Earlier work this paper cites.
Banani, M.E., Corso, J.J., Fouhey, D.F.: Novel object viewpoint estimation through reconstruction alignment. In: CVPR (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Cai, M., Reid, I.: Reconstruct locally, localize globally: A model free method for object pose estimation. In: CVPR (2020)
2020
Cited alongside, same era.
Chen, D., Li, J., Wang, Z., Xu, K.: Learning canonical shape space for category-level 6d object pose and size estimation. In: CVPR (2020)
2020
Cited alongside, same era.
Chen, X., Dong, Z., Song, J., Geiger, A., Hilliges, O.: Category level object pose estimation via neural analysis-by-synthesis. In: ECCV (2020)
2020
Cited alongside, same era.
Grabner, A., Wang, Y., Zhang, P., Guo, P., Xiao, T., Vajda, P., Roth, P.M., Lepetit, V.: Geometric correspondence fields: Learned differentiable rendering for 3d pose refinement in the wild. In: ECCV (2020)
2020
Cited alongside, same era.
Hodan, T., Barath, D., Matas, J.: Epos: Estimating 6d pose of objects with symmetries. In: CVPR (2020)
2020
Lin, J., Wei, Z., Li, Z., Xu, S., Jia, K., Li, Y.: Dualposenet: Category-level 6d object pose and size estimation using dual pose network with refined learning of pose consistency. In: ICCV (2021)
2021
Later among the works it cites.
Liu, X., Iwase, S., Kitani, K.M.: Stereobj-1m: Large-scale stereo image dataset for 6d object pose estimation. In: CVPR (2021)
2021
Later among the works it cites.
Mercier, J.P., Garon, M., Giguere, P., Lalonde, J.F.: Deep template-based object instance detection. In: WACV (2021)
2021
Later among the works it cites.
Okorn, B., Gu, Q., Hebert, M., Held, D.: Zephyr: Zero-shot pose hypothesis rating. In: ICRA (2021)
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Hodaň, T., Sundermeyer, M., Drost, B., Labbé, Y., Brachmann, E., Michel, F., Rother, C., Matas, J.: Bop challenge 2020 on 6d object localization. In: ECCV (2020)
2020
Cited alongside, same era.
Hu, Y., Fua, P., Wang, W., Salzmann, M.: Single-stage 6d object pose estimation. In: CVPR (2020)
2020
Cited alongside, same era.
Labbé, Y., Carpentier, J., Aubry, M., Sivic, J.: Cosypose: Consistent multi-view multi-object 6d pose estimation. In: ECCV (2020)
2020
Cited alongside, same era.
Liu, X., Jonschkowski, R., Angelova, A., Konolige, K.: Keypose: Multi-view 3d labeling and keypoint estimation for transparent objects. In: CVPR (2020)
2020
Cited alongside, same era.
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: ECCV (2020)
2020
Cited alongside, same era.
Osokin, A., Sumin, D., Lomakin, V.: Os2d: One-stage one-shot object detection by matching anchor features. In: ECCV (2020)
2020
Cited alongside, same era.
Park, K., Mousavian, A., Xiang, Y., Fox, D.: Latentfusion: End-to-end differentiable reconstruction and rendering for unseen object pose estimation. In: CVPR (2020)
2020
Cited alongside, same era.
2021
Later among the works it cites.
Reizenstein, J., Shapovalov, R., Henzler, P., Sbordone, L., Labatut, P., Novotny, D.: Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In: CVPR (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Wang, G., Manhardt, F., Tombari, F., Ji, X.: Gdr-net: Geometry-guided direct regression network for monocular 6d object pose estimation. In: CVPR (2021)
2021
Later among the works it cites.
Wang, Q., Wang, Z., Genova, K., Srinivasan, P.P., Zhou, H., Barron, J.T., Martin-Brualla, R., Snavely, N., Funkhouser, T.: Ibrnet: Learning multi-view image-based rendering. In: CVPR (2021)
2021
Later among the works it cites.
Wen, B., Bekris, K.: Bundletrack: 6d pose tracking for novel objects without instance or category-level 3d models. In: IROS (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Yen-Chen, L., Florence, P., Barron, J.T., Rodriguez, A., Isola, P., Lin, T.Y.: inerf: Inverting neural radiance fields for pose estimation. In: IROS (2021)
2021
Later among the works it cites.
Cai, D., Heikkilä, J., Rahtu, E.: Ove6d: Object viewpoint encoding for depth-based 6d object pose estimation. In: CVPR (2022)
2022
Closest in time.
Deng, X., Geng, J., Bretl, T., Xiang, Y., Fox, D.: icaps: Iterative category-level object pose and shape estimation. IEEE Robotics and Automation Letters (2022)
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Gu, Q., Okorn, B., Held, D.: Ossid: Online self-supervised instance detection by (and for) pose estimation. IEEE Robotics and Automation Letters (2022)
2022
Closest in time.
He, Y., Wang, Y., Fan, H., Sun, J., Chen, Q.: Fs6d: Few-shot 6d pose estimation of novel objects. In: CVPR (2022)
2022
Closest in time.
Nguyen, V.N., Hu, Y., Xiao, Y., Salzmann, M., Lepetit, V.: Templates for 3d object pose estimation revisited: Generalization to new objects and robustness to occlusions. In: CVPR (2022)
2022
Closest in time.
Ponimatkin, G., Labbé, Y., Russell, B., Aubry, M., Sivic, J.: Focal length and object pose estimation via render and compare. In: CVPR (2022)
2022
Closest in time.
Shugurov, I., Li, F., Busam, B., Ilic, S.: Osop: A multi-stage one shot object pose estimation framework. In: CVPR (2022)
2022
Closest in time.
Su, Y., Saleh, M., Fetzer, T., Rambach, J., Navab, N., Busam, B., Stricker, D., Tombari, F.: Zebrapose: Coarse to fine surface encoding for 6dof object pose estimation. In: CVPR (2022)
2022
Closest in time.
Sun, J., Wang, Z., Zhang, S., He, X., Zhao, H., Zhang, G., Zhou, X.: OnePose: One-shot object pose estimation without CAD models. CVPR (2022)
2022
Closest in time.