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Representing scenes at the granularity of objects is a prerequisite for scene understanding and decision making.
MONet: Unsupervised scene decomposition and representation
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Shapenet: An information-rich 3d model repository
Chang, A.X., Funkhouser, T.A., Guibas, L.J., Hanrahan, P., Huang, Q.X., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F., 2015 · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks, in: Advances in Neural Information Processing Systems (NeurIPS)
Ren, S., He, K., Girshick, R., Sun, J., 2015 · 2015
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3d-r2n2: A unified approach for single and multi-view 3D object reconstruction, in: Proc. European Conference on Computer Vision (ECCV)
Choy, C.B., Xu, D., Gwak, J., Chen, K., Savarese, S., 2016 · 2016
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Attend, infer, repeat: Fast scene understanding with generative models, in: Advances in Neural Information Processing Systems (NeurIPS)
Eslami, S.M.A., Heess, N., Weber, T., Tassa, Y., Szepesvari, D., Kavukcuoglu, K., Hinton, G.E., 2016 · 2016
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Unsupervised learning of 3D structure from images, in: Advances in Neural Information Processing Systems
Jimenez Rezende, D., Eslami, S.M.A., Mohamed, S., Battaglia, P., Jaderberg, M., Heess, N., 2016 · 2016
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Learning physical intuition of block towers by example, in: International Conference on Machine Learning (ICML)
Lerer, A., Gross, S., Fergus, R., 2016 · 2016
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Volumetric and multi-view cnns for object classification on 3d data, in: Proc. Computer Vision and Pattern Recognition (CVPR), IEEE
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You only look once: Unified, real-time object detection, in: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016 · 2016
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Learning detailed face reconstruction from a single image, in: Proceedings of the Int. Conf. on Computer Vision and Pattern Recognition (CVPR)
Richardson, E., Sela, M., Or-El, R., Kimmel, R., 2016 · 2016
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Multi-view 3d models from single images with a convolutional network, in: Proc. European Conference on Computer Vision (ECCV)
Tatarchenko, M., Dosovitskiy, A., Brox, T., 2016 · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling, in: Advances in Neural Information Processing Systems (NeurIPS)
Wu, J., Zhang, C., Xue, T., Freeman, W.T., Tenenbaum, J.B., 2016 · 2016
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Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision, in: Advances in Neural Information Processing Systems (NeurIPS)
Yan, X., Yang, J., Yumer, E., Guo, Y., Lee, H., 2016 · 2016
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3d shape induction from 2d views of multiple objects, in: International Conference on 3D Vision (3DV)
Gadelha, M., Maji, S., Wang, R., 2017 · 2017
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Weakly supervised 3d reconstruction with adversarial constraint, in: International Conference on 3D Vision (3DV)
Gwak, J., Choy, C.B., Chandraker, M., Garg, A., Savarese, S., 2017 · 2017
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C.L., Girshick, R.B., 2017 · 2017
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Learning a multi-view stereo machine, in: Advances in Neural Information Processing Systems (NeurIPS)
Kar, A., Häne, C., Malik, J., 2017 · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation, in: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR)
Qi, C.R., Su, H., Mo, K., Guibas, L.J., 2017 · 2017
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Multi-view supervision for single-view reconstruction via differentiable ray consistency, in: Proc. of the IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR)
Occupancy networks: Learning 3d reconstruction in function space, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A., 2019 · 2019
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Texture fields: Learning texture representations in function space, in: Proc. IEEE/CVF International Conference on Computer Vision (ICCV)
Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., Geiger, A., 2019 · 2019
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Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S., 2019 · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations, in: Advances in Neural Information Processing Systems (NeurIPS)
Sitzmann, V., Zollhöfer, M., Wetzstein, G., 2019 · 2019
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Tulsiani, S., Zhou, T., Efros, A., Malik, J., 2017 · 2017
Cited alongside, same era.
MarrNet: 3D shape reconstruction via 2.5D sketches, in: Advances In Neural Information Processing Systems (NeurIPS)
Wu, J., Wang, Y., Xue, T., Sun, X., Freeman, W.T., Tenenbaum, J.B., 2017 · 2017
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PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes, in: Robotics: Science and Systems (RSS)
Xiang, Y., Schmidt, T., Narayanan, V., Fox, D., 2017 · 2017
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Learning representations and generative models for 3D point clouds, in: Proc. of the International Conference on Machine Learning (ICML)
Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L., 2018 · 2018
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Synthesizing robust adversarial examples, in: Int. Conf. on Machine Learning (ICML)
Athalye, A., Engstrom, L., Ilyas, A., Kwok, K., 2018 · 2018
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Neural scene representation and rendering
Eslami, S.M.A., Jimenez Rezende, D., Besse, F., Viola, F., Morcos, A.S., Garnelo, M., Ruderman, A., Rusu, A.A., Danihelka, I., Gregor, K., Reichert, D.P., Buesing, L., Weber, T., Vinyals, O., Rosenbaum, D., Rabinowitz, N., King, H., Hillier, C., Botvinick, M., Wierstra, D., Kavukcuoglu, K., Hassabis, D., 2018 · 2018
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AtlasNet: A papier-mâché approach to learning 3d surface generation, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M., 2018 · 2018
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Neural 3d mesh renderer, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Kato, H., Ushiku, Y., Harada, T., 2018 · 2018
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Pix2vox: Context-aware 3d reconstruction from single and multi-view images, in: Proc. IEEE/CVF International Conference on Computer Vision (ICCV)
Xie, H., Yao, H., Sun, X., Zhou, S., Zhang, S., 2019 · 2019
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DISN: deep implicit surface network for high-quality single-view 3d reconstruction, in: Advances in Neural Information Processing Systems (NeurIPS)
Xu, Q., Wang, W., Ceylan, D., Mech, R., Neumann, U., 2019 · 2019
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Differentiable surface splatting for point-based geometry processing
Yifan, W., Serena, F., Wu, S., Öztireli, C., Sorkine-Hornung, O., 2019 · 2019
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On the continuity of rotation representations in neural networks, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H., 2019 · 2019
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GENESIS: Generative scene inference and sampling with object-centric latent representations, in: International Conference on Learning Representations (ICLR)
Engelcke, M., Kosiorek, A.R., Jones, O.P., Posner, I., 2020 · 2020
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Unsupervised object-centric video generation and decomposition in 3D, in: Advances in Neural Information Processing Systems (NeurIPS)
Henderson, P., Lampert, C.H., 2020 · 2020
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Leveraging 2d data to learn textured 3d mesh generation, in: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)
Henderson, P., Tsiminaki, V., Lampert, C.H., 2020 · 2020
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Image-to-voxel model translation for 3d scene reconstruction and segmentation, in: Proc. European Conference on Computer Vision (ECCV)
Kniaz, V.A., Knyaz, V.V., Remondino, F., Bordodymov, A., Moshkantsev, P., 2020 · 2020
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Learning object-centric representations of multi-object scenes from multiple views, in: Advances in Neural Information Processing Systems (NeurIPS)
Li, N., Eastwood, C., Fisher, R.B., 2020 · 2020
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Towards unsupervised learning of generative models for 3d controllable image synthesis, in: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)
Liao, Y., Schwarz, K., Mescheder, L., Geiger, A., 2020 · 2020
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SPACE: Unsupervised object-oriented scene representation via spatial attention and decomposition, in: Int. Conf. on Learning Representations (ICLR)
Lin, Z., Wu, Y.F., Peri, S.V., Sun, W., Singh, G., Deng, F., Jiang, J., Ahn, S., 2020 · 2020
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Object-centric learning with slot attention, in: Advances in Neural Information Processing Systems (NeurIPS)
Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., Kipf, T., 2020 · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis, in: Proc. European Conference on Computer Vision (ECCV)
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R., 2020 · 2020
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Blockgan: Learning 3d object-aware scene representations from unlabelled images, in: Advances in Neural Information Processing Systems (NeurIPS)
Nguyen-Phuoc, T., Richardt, C., Mai, L., Yang, Y.L., Mitra, N., 2020 · 2020
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State of the art on neural rendering
Tewari, A., Fried, O., Thies, J., Sitzmann, V., Lombardi, S., Sunkavalli, K., Martin-Brualla, R., Simon, T., Saragih, J., Nießner, M., Pandey, R., Fanello, S., Wetzstein, G., Zhu, J.Y., Theobalt, C., Agrawala, M., Shechtman, E., Goldman, D.B., Zollhöfer, M., 2020 · 2020
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Directshape: Photometric alignment of shape priors for visual vehicle pose and shape estimation, in: Proc. IEEE International Conference on Robotics and Automation (ICRA)
Wang, R., Yang, N., Stueckler, J., Cremers, D., 2020 · 2020
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Learning to manipulate individual objects in an image, in: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)
Yang, Y., Chen, Y., Soatto, S., 2020 · 2020
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Roots: Object-centric representation and rendering of 3d scenes
Chen, C., Deng, F., Ahn, S., 2021 · 2021
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Giraffe: Representing scenes as compositional generative neural feature fields, in: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)
Niemeyer, M., Geiger, A., 2021 · 2021
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Decomposing 3d scenes into objects via unsupervised volume segmentation
Stelzner, K., Kersting, K., Kosiorek, A.R., 2021 · 2021
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