Fetching the paper…
Reading the bibliography…
Implicit surface representations, such as signed-distance functions, combined with deep learning have led to impressive models which can represent detailed shapes of objects with arbitrary topology.
Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3D surface construction algorithm. In: Conference on Computer Graphics and Interactive Techniques (1987)
1987
Earlier work this paper cites.
Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: SIGGRAPH (1996)
1996
Earlier work this paper cites.
Ohtake, Y., Belyaev, A., Alexa, M., Turk, G., Seidel, H.P.: Multi-level partition of unity implicits. In: ACM Transactions on Graphics (TOG) (2003)
2003
Earlier work this paper cites.
Handa, A., Whelan, T., McDonald, J., Davison, A.: A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. In: International Conference on Robotics and Automation (ICRA) (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)
2015
Earlier work this paper cites.
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: European Conference on Computer Vision (ECCV) (2016)
2016
Earlier work this paper cites.
Salimans, T., Kingma, D.P.: Weight normalization: A simple reparameterization to accelerate training of deep neural networks. In: Advances in Neural Information Processing Systems (NeurIPS) (2016)
2016
Earlier work this paper cites.
Bogo, F., Romero, J., Pons-Moll, G., Black, M.J.: Dynamic FAUST: Registering human bodies in motion. In: Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Riegler, G., Osman Ulusoy, A., Geiger, A.: OctNet: Learning deep 3D representations at high resolutions. In: Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Earlier work this paper cites.
Tulsiani, S., Su, H., Guibas, L.J., Efros, A.A., Malik, J.: Learning shape abstractions by assembling volumetric primitives. In: Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Earlier work this paper cites.
Groueix, T., Fisher, M., Kim, V., Russell, B., Aubry, M.: A papier-mache approach to learning 3D surface generation. In: Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Cited alongside, same era.
Kato, H., Ushiku, Y., Harada, T.: Neural 3D mesh renderer. In: Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Cited alongside, same era.
Stutz, D., Geiger, A.: Learning 3D shape completion under weak supervision. In: International Journal of Computer Vision (IJCV) (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: European Conference on Computer Vision (ECCV) (2018)
2018
Cited alongside, same era.
Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Computer Vision and Pattern Recognition (CVPR) (2019)
Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Occupancy flow: 4D reconstruction by learning particle dynamics. In: International Conference on Computer Vision (CVPR) (2019)
2019
Later among the works it cites.
Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: Learning continuous signed distance functions for shape representation. In: Computer Vision and Pattern Recognition (CVPR) (2019)
2019
Later among the works it cites.
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
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: International Conference on Computer Vision (ICCV) (2019)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
Deng, B., Genova, K., Yazdani, S., Bouaziz, S., Hinton, G., Tagliasacchi, A.: Cvxnets: Learnable convex decomposition. In: Advances in Neural Information Processing Systems Workshops (2019)
2019
Cited alongside, same era.
Deprelle, T., Groueix, T., Fisher, M., Kim, V., Russell, B., Aubry, M.: Learning elementary structures for 3D shape generation and matching. In: Advances in Neural Information Processing Systems (NeurIPS) (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: International Conference on Computer Vision (ICCV) (2019)
2019
Cited alongside, same era.
Liu, S., Saito, S., Chen, W., Li, H.: Learning to infer implicit surfaces without 3D supervision. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
Cited alongside, same era.
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: Learning 3D reconstruction in function space. In: Computer Vision and Pattern Recognition (CVPR) (2019)
2019
Cited alongside, same era.
Michalkiewicz, M., Pontes, J.K., Jack, D., Baktashmotlagh, M., Eriksson, A.: Implicit surface representations as layers in neural networks. In: International Conference on Computer Vision (ICCV) (2019)
2019
Cited alongside, same era.
2019
Later among the works it cites.
Shimada, S., Golyanik, V., Tretschk, E., Stricker, D., Theobalt, C.: DispVoxNets: Non-rigid point set alignment with supervised learning proxies. In: International Conference on 3D Vision (3DV) (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.
Atzmon, M., Lipman, Y.: Sal: Sign agnostic learning of shapes from raw data. In: Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Closest in time.
Deng, B., Lewis, J., Jeruzalski, T., Pons-Moll, G., Hinton, G., Norouzi, M., Tagliasacchi, A.: Nasa: Neural articulated shape approximation (2020)
2020
Closest in time.
Genova, K., Cole, F., Sud, A., Sarna, A., Funkhouser, T.: Local deep implicit functions for 3d shape. In: Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Closest in time.
Tretschk, E., Tewari, A., Zollhöfer, M., Golyanik, V., Theobalt, C.: DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects. European Conference on Computer Vision (ECCV) (2020)
2020
Closest in time.
Williams, F., Parent-Levesque, J., Nowrouzezahrai, D., Panozzo, D., Moo Yi, K., Tagliasacchi, A.: Voronoinet: General functional approximators with local support. In: Computer Vision and Pattern Recognition Workshops (CVPRW) (2020)
2020
Closest in time.