Fetching the paper…
Reading the bibliography…
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.
Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence - Volume 2. pp. 659–663. IJCAI’77, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1977),
1977
Earlier work this paper cites.
Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. In: SIGGRAPH (1987)
1987
Earlier work this paper cites.
Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In: Proc. of the Eurographics symposium on Geometry processing (2006)
2006
Earlier work this paper cites.
Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Transactions on Graphics (ToG) (2013)
2013
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: Proc. of ECCV (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
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: Proc. of ECCV (2016)
2016
Earlier work this paper cites.
Girdhar, R., Fouhey, D., Rodriguez, M., Gupta, A.: Learning a predictable and generative vector representation for objects. In: Proc. of ECCV (2016)
2016
Earlier work this paper cites.
Yan, X., Yang, J., Yumer, E., Guo, Y., Lee, H.: Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision. In: Proc. of NeurIPS (2016)
2016
Earlier work this paper cites.
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence (2017)
2017
Earlier work this paper cites.
Fan, H., Su, H., Guibas, L.: A point set generation network for 3d object reconstruction from a single image. In: Proc. of CVPR (2017)
2017
Earlier work this paper cites.
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. 605–613 (2017)
2017
Earlier work this paper cites.
Kar, A., Häne, C., Malik, J.: Learning a multi-view stereo machine. In: Proc. of NeurIPS (2017)
2017
Earlier work this paper cites.
Tatarchenko, M., Dosovitskiy, A., Brox, T.: Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs. In: Proc. of ICCV (2017)
2017
Earlier work this paper cites.
Tulsiani, S., Zhou, T., Efros, A.A., Malik, J.: Multi-view supervision for single-view reconstruction via differentiable ray consistency. In: Proc. of CVPR (2017)
2017
Earlier work this paper cites.
Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: Atlasnet: A papier-mâché approach to learning 3d surface generation. In: Proc. of CVPR (2018)
2018
Earlier work this paper cites.
Insafutdinov, E., Dosovitskiy, A.: Unsupervised learning of shape and pose with differentiable point clouds. In: Proc. of NeurIPS (2018)
2018
Cited alongside, same era.
Kanazawa, A., Tulsiani, S., Efros, A.A., Malik, J.: Learning category-specific mesh reconstruction from image collections. In: Proc. of ECCV (2018)
2018
Cited alongside, same era.
Kato, H., Ushiku, Y., Harada, T.: Neural 3d mesh renderer. In: Proc. of CVPR (2018)
2018
Cited alongside, same era.
Lin, C.H., Kong, C., Lucey, S.: Learning efficient point cloud generation for dense 3d object reconstruction. In: Proc. of AAAI (2018)
2018
Cited alongside, same era.
Richter, S.R., Roth, S.: Matryoshka networks: Predicting 3D geometry via nested shape layers. In: Proc. of CVPR (2018)
2018
Cited alongside, same era.
Kulkarni, N., Gupta, A., Tulsiani, S.: Canonical surface mapping via geometric cycle consistency. In: Proc. of ICCV (2019)
2019
Later among the works it cites.
Lin, C.H., Wang, O., Russell, B.C., Shechtman, E., Kim, V.G., Fisher, M., Lucey, S.: Photometric mesh optimization for video-aligned 3d object reconstruction. In: Proc. of CVPR (2019)
2019
Later among the works it cites.
Liu, S., Li, T., Chen, W., Li, H.: Soft rasterizer: A differentiable renderer for image-based 3d reasoning (2019)
2019
Later among the works it cites.
Liu, S., Saito, S., Chen, W., Li, H.: Learning to infer implicit surfaces without 3d supervision. In: Advances in Neural Information Processing Systems. pp. 8295–8306 (2019)
2019
Later among the works it cites.
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: Learning 3d reconstruction in function space. In: Proc. of CVPR (2019)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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 ECCV (2018)
2018
Cited alongside, same era.
Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes. In: SIGGRAPH Asia (2018)
2018
Cited alongside, same era.
Bhoi, A.: Monocular depth estimation: A survey. arXiv preprint arXiv:1901.09402 (2019)
2019
Cited alongside, same era.
Chen, R., Han, S., Xu, J., Su, H.: Point-based multi-view stereo network. In: Proc. of ICCV (2019)
2019
Cited alongside, same era.
Chen, R., Han, S., Xu, J., Su, H.: Point-based multi-view stereo network. In: Proc. of ICCV (2019)
2019
Cited alongside, same era.
Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Proc. of CVPR (2019)
2019
Cited alongside, same era.
Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Proc. of CVPR (2019)
2019
Cited alongside, same era.
2019
Later among the works it cites.
Pan, J., Han, X., Chen, W., Tang, J., Jia, K.: Deep mesh reconstruction from single rgb images via topology modification networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 9964–9973 (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: Proc. of CVPR (2019)
2019
Later among the works it cites.
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 ICCV (2019)
2019
Later among the works it cites.
Sridhar, S., Rempe, D., Valentin, J., Bouaziz, S., Guibas, L.J.: Multiview aggregation for learning category-specific shape reconstruction. In: Proc. of 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. of CVPR (2019)
2019
Later among the works it 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: Proc. of CVPR (2019)
2019
Later among the works it cites.
Wang, W., Ceylan, D., Mech, R., Neumann, U.: 3dn: 3d deformation network. In: Proc. of 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 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: Proc. of NeurIPS (2019)
2019
Later among the works it cites.
Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3504–3515 (2020)
2020
Closest in time.