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A collection of 3D reconstruction papers in the deep learning era.
| Date | Stars |
|---|---|
| 2026-07-31 | 910 |
| 2026-08-03 | 910 |
| 2026-08-06 | 910 |
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# Awesome 3D Reconstruction Papers
[](https://awesome.re)
A collection of 3D reconstruction papers in the deep learning era. Feel free to contribute :)
Table of Contents
=================
* [Object-level](#object-level)
* [Single-view](#single-view)
* [Multi-view](#multi-view)
* [Unsupervised](#unsupervised)
* [Scene-level](#scene-level)
* [Single-view](#single-view-1)
* [Multi-view](#multi-view-1)
* [Neural-Surface](#neural-surface)
* [Multi-view](#multi-view-2)
* [Point-cloud](#point-cloud)
* [RGB-D](#rgb-d)
* [Survey](#survey)
## Object-level
### Single-view
| Paper | Representation| Publisher | Project/Code |
| :----------------------------------------------------------: | :-------: | :-------: | :-----------------------------------------------------: |
| [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://openaccess.thecvf.com/content_cvpr_2017/html/Fan_A_Point_Set_CVPR_2017_paper.html) | Point Cloud | CVPR 2017 | [Code](https://github.com/fanhqme/PointSetGeneration) |
| [SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks](https://openaccess.thecvf.com/content_cvpr_2017/html/Sinha_SurfNet_Generating_3D_CVPR_2017_paper.html) | Mesh | CVPR 2017 | [Code](https://github.com/sinhayan/surfnet) |
| [OctNet: Learning Deep 3D Representations at High Resolutions](https://openaccess.thecvf.com/content_cvpr_2017/html/Riegler_OctNet_Learning_Deep_CVPR_2017_paper.html) | Voxel | CVPR 2017 | [Code](https://github.com/griegler/octnet) |
| [Rethinking Reprojection: Closing the Loop for Pose-Aware Shape Reconstruction From a Single Image](https://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Rethinking_Reprojection_Closing_ICCV_2017_paper.html) | Voxel | ICCV 2017 | / |
| [MarrNet: 3D Shape Reconstruction via 2.5D Sketches](https://proceedings.neurips.cc/paper/2017/hash/ad972f10e0800b49d76fed33a21f6698-Abstract.html) | Voxel | NIPS 2017 | [Project](http://marrnet.csail.mit.edu/) |
| [Hierarchical Surface Prediction for 3D Object Reconstruction](https://arxiv.org/abs/1704.00710) | Voxel | 3DV 2017 | [Code](https://github.com/chaene/hsp) |
| [Image2Mesh: A Learning Framework for Single Image 3D Reconstruction](https://arxiv.org/abs/1711.10669) | Mesh | ACCV 2018 | [Code](https://github.com/jhonykaesemodel/image2mesh) |
| [Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction](https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16530) | Point Cloud | AAAI 2018 | [Project](https://chenhsuanlin.bitbucket.io/3D-point-cloud-generation/) |
| [A Papier-Mâché Approach to Learning 3D Surface Generation](https://openaccess.thecvf.com/content_cvpr_2018/html/Groueix_A_Papier-Mache_Approach_CVPR_2018_paper.html) | Mesh | CVPR 2018 | [Project](http://imagine.enpc.fr/~groueixt/atlasnet/) |
| [Pixels, voxels, and views: A study of shape representations for single view 3D object shape prediction](https://openaccess.thecvf.com/content_cvpr_2018/html/Shin_Pixels_Voxels_and_CVPR_2018_paper.html) | Generic | CVPR 2018 | [Project](https://www.ics.uci.edu/~daeyuns/pixels-voxels-views/) |
| [Im2Struct: Recovering 3D Shape Structure From a Single RGB Image](https://openaccess.thecvf.com/content_cvpr_2018/html/Niu_Im2Struct_Recovering_3D_CVPR_2018_paper.html) | Parts | CVPR 2018 | [Code](https://github.com/chengjieniu/Im2Struct) |
| [Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers](https://openaccess.thecvf.com/content_cvpr_2018/html/Richter_Matryoshka_Networks_Predicting_CVPR_2018_paper.html) | Voxel | CVPR 2018 | [Code](https://bitbucket.org/visinf/projects-2018-matryoshka/src/master/) |
| [Multi-View Consistency as Supervisory Signal for Learning Shape and Pose Prediction](https://openaccess.thecvf.com/content_cvpr_2018/html/Tulsiani_Multi-View_Consistency_as_CVPR_2018_paper.html) | Voxel | CVPR 2018 | [Project](https://shubhtuls.github.io/mvcSnP/) |
| [Efficient Dense PoExcerpt of 69,977 characters
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Noah Stier · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3d70ef84c5e06a71, name:3d reconstruction, desc:3d reconstruction