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Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction.
Kazhdan, M.M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. on Graphics 32
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Chang, A.X., Funkhouser, T.A., Guibas, L.J., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: Shapenet: An information-rich 3d model repository. arXiv.org 1512.03012
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Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Proc. of the International Conf. on Machine learning (ICML) (2015)
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Maturana, D., Scherer, S.: Voxnet: A 3d convolutional neural network for real-time object recognition. In: Proc. IEEE International Conf. on Intelligent Robots and Systems (IROS) (2015)
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2015)
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Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2015)
2015
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Choy, C.B., Xu, D., Gwak, J., Chen, K., Savarese, S.: 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In: Proc. of the European Conf. on Computer Vision (ECCV) (2016)
2016
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Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: Learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2016)
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Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In: Advances in Neural Information Processing Systems (NeurIPS) (2016)
2016
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Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A., Zhang, Y.: Matterport3D: Learning from RGB-D data in indoor environments. Proc. of the International Conf. on 3D Vision (3DV) (2017)
2017
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Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Niessner, M.: Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
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Dai, A., Qi, C.R., Nießner, M.: Shape completion using 3d-encoder-predictor cnns and shape synthesis. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
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Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3d object reconstruction from a single image. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
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Hane, C., Tulsiani, S., Malik, J.: Hierarchical surface prediction for 3d object reconstruction. In: Proc. of the International Conf. on 3D Vision (3DV) (2017)
2017
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Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
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Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems (NeurIPS) (2017)
2017
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Riegler, G., Ulusoy, A.O., Bischof, H., Geiger, A.: OctNetFusion: Learning depth fusion from data. In: Proc. of the International Conf. on 3D Vision (3DV) (2017)
2017
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Riegler, G., Ulusoy, A.O., Geiger, A.: Octnet: Learning deep 3d representations at high resolutions. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Cited alongside, same era.
Tatarchenko, M., Dosovitskiy, A., Brox, T.: Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2017)
2017
Cited alongside, same era.
Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: AtlasNet: A papier-mâché approach to learning 3d surface generation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Cited alongside, same era.
Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., Geiger, A.: Texture fields: Learning texture representations in function space. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2019)
2019
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Park, J.J., Florence, P., Straub, J., Newcombe, R.A., Lovegrove, S.: Deepsdf: Learning continuous signed distance functions for shape representation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
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Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
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Prokudin, S., Lassner, C., Romero, J.: Efficient learning on point clouds with basis point sets. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2019)
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Kanazawa, A., Tulsiani, S., Efros, A.A., Malik, J.: Learning category-specific mesh reconstruction from image collections. In: Proc. of the European Conf. on Computer Vision (ECCV) (2018)
2018
Cited alongside, same era.
Liao, Y., Donne, S., Geiger, A.: Deep marching cubes: Learning explicit surface representations. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Cited alongside, same era.
Lin, C., Kong, C., Lucey, S.: Learning efficient point cloud generation for dense 3d object reconstruction. In: Proc. of the Conf. on Artificial Intelligence (AAAI) (2018)
2018
Cited alongside, same era.
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., Jiang, Y.G.: Pixel2mesh: Generating 3d mesh models from single rgb images. In: Proc. of the European Conf. on Computer Vision (ECCV) (2018)
2018
Cited alongside, same era.
Zhang, Q., Wu, Y.N., Zhu, S.: Interpretable convolutional neural networks. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Cited alongside, same era.
Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
Cited alongside, same era.
Genova, K., Cole, F., Vlasic, D., Sarna, A., Freeman, W.T., Funkhouser, T.: Learning shape templates with structured implicit functions. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2019)
2019
Cited alongside, same era.
Gkioxari, G., Malik, J., Johnson, J.: Mesh R-CNN. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2019)
2019
Cited alongside, same era.
2019
Later among the works it cites.
Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., Li, H.: Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2019)
2019
Later among the works it cites.
Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: Continuous 3d-structure-aware neural scene representations. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
Later among the works it cites.
Tatarchenko, M., Richter, S.R., Ranftl, R., Li, Z., Koltun, V., Brox, T.: What do single-view 3d reconstruction networks learn? In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
Later among the works it cites.
Wen, C., Zhang, Y., Li, Z., Fu, Y.: Pixel2mesh++: Multi-view 3d mesh generation via deformation. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2019)
2019
Later among the works it cites.
Xu, Q., Wang, W., Ceylan, D., Mech, R., Neumann, U.: DISN: deep implicit surface network for high-quality single-view 3d reconstruction. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
Later among the works it cites.
Yang, G., Huang, X., Hao, Z., Liu, M., Belongie, S.J., Hariharan, B.: Pointflow: 3d point cloud generation with continuous normalizing flows. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2019)
2019
Later among the works it cites.
Chibane, J., Alldieck, T., Pons-Moll, G.: Implicit functions in feature space for 3d shape reconstruction and completion. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Closest in time.
Genova, K., Cole, F., Sud, A., Sarna, A., Funkhouser, T.A.: Local deep implicit functions for 3d shape. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Closest in time.
Jeruzalski, T., Deng, B., Norouzi, M., Lewis, J.P., Hinton, G.E., Tagliasacchi, A.: NASA: neural articulated shape approximation. In: Proc. of the European Conf. on Computer Vision (ECCV) (2020)
2020
Closest in time.
Jiang, C., Sud, A., Makadia, A., Huang, J., Nießner, M., Funkhouser, T.: Local implicit grid representations for 3d scenes. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Closest in time.
Liu, S., Zhang, Y., Peng, S., Shi, B., Pollefeys, M., Cui, Z.: DIST: rendering deep implicit signed distance function with differentiable sphere tracing. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Closest in time.
Niemeyer, M., Mescheder, L.M., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Closest in time.
Paschalidou, D., van Gool, L., Geiger, A.: Learning unsupervised hierarchical part decomposition of 3d objects from a single rgb image. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Closest in time.