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https://github.com/chenzhaiyu/awesome-planar-reconstruction
A curated list on piecewise-planar reconstruction
https://github.com/chenzhaiyu/awesome-planar-reconstruction
List: awesome-planar-reconstruction
3d planar reconstruction
Last synced: 3 months ago
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A curated list on piecewise-planar reconstruction
- Host: GitHub
- URL: https://github.com/chenzhaiyu/awesome-planar-reconstruction
- Owner: chenzhaiyu
- License: cc-by-4.0
- Created: 2020-07-30T09:58:14.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-02-06T05:51:31.000Z (over 1 year ago)
- Last Synced: 2024-05-19T18:57:00.942Z (6 months ago)
- Topics: 3d, planar, reconstruction
- Homepage:
- Size: 30.3 KB
- Stars: 151
- Watchers: 12
- Forks: 16
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-scene-understanding - Awesome Planar Reconstruction
- ultimate-awesome - awesome-planar-reconstruction - A curated list on piecewise-planar reconstruction. (Other Lists / PowerShell Lists)
README
# Awesome Planar Reconstruction [](https://github.com/sindresorhus/awesome)
## Papers
Those with piece-wise planarity are **highlighted**.
### Plane Extraction / Detection
| Title | Authors | Venue | Year | Resources |
| :----------------------------------------------------------- | ---------------------------- | ----- | ---- | ------------------------------------------------------------ |
| Efficient RANSAC for Point-Cloud Shape Detection | Ruwen Schnabel et al. | Computer Graphics Forum | 2007 | [[PDF]](https://cg.cs.uni-bonn.de/en/publications/paper-details/schnabel-2007-efficient/) [[CODE]](https://cg.cs.uni-bonn.de/en/publications/paper-details/schnabel-2007-efficient/) |
| The 3D Hough Transform for Plane Detection in Point Clouds: A Review and a new Accumulator Design | Dorit Borrmann et al. | 3D Research | 2011 | [[PDF]](https://www.researchgate.net/publication/228754371_The_3D_Hough_Transform_for_plane_detection_in_point_clouds_A_review_and_a_new_accumulator_design) |
| Planar Shape Detection at Structural Scales | Hao Fang et al. | CVPR | 2018 | [[PDF]](https://hal.inria.fr/hal-01741650/document) |
| Plane Segmentation Based on the Optimal-vector-field in LiDAR Point Clouds | Sheng Xu et al. | PAMI | 2020 | [[PDF]](https://ieeexplore.ieee.org/document/9095372)|
| From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds | Christiane Sommer et al. | ICRA | 2020 | [[PDF]](https://arxiv.org/ftp/arxiv/papers/2001/2001.07360.pdf) [[CODE]](https://github.com/c-sommer/orthogonal-planes) |
| Supervised Fitting of Geometric Primitives to 3D Point Clouds | Lingxiao Li et al. | CVPR | 2019 | [[PDF]](https://arxiv.org/pdf/1811.08988.pdf) [[CODE]](https://github.com/lingxiaoli94/SPFN) |
| Highly Parallelizable Plane Extraction for Organized Point Clouds Using Spherical Convex Hulls | Hannes Möls et al. | ICRA | 2020 | [[PDF]](https://isas.iar.kit.edu/pdf/ICRA20_Li.pdf) |
| CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds | Eric-Tuan Lê et al. | ICCV | 2021 | [[PDF]](https://arxiv.org/pdf/2109.00113.pdf) [[CODE]](https://github.com/erictuanle/CPFN) |
### Single-view Reconstruction
| Title | Authors | Venue | Year | Resources |
| :----------------------------------------------------------- | --------------------------------- | ----- | ---- | ------------------------------------------------------------ |
| **PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image** | Chen Liu et al. | CVPR | 2018 | [[PDF]](https://arxiv.org/pdf/1804.06278.pdf) [[CODE]](https://github.com/art-programmer/PlaneNet) |
| **Recovering 3D Planes from a Single Image via Convolutional Neural Networks** | Fengting Yang and Zihan Zhou | ECCV | 2018 | [[PDF]](https://faculty.ist.psu.edu/zzhou/paper/ECCV18-plane.pdf) [[CODE]](https://github.com/fuy34/planerecover) |
| **PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image** | Chen Liu et al. | CVPR | 2019 | [[PDF]](https://arxiv.org/pdf/1812.04072.pdf) [[CODE]](https://github.com/NVlabs/planercnn) |
| **Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding** | Zehao Yu et al. | CVPR | 2019 | [[PDF]](https://arxiv.org/pdf/1902.09777.pdf) [[CODE]](https://github.com/svip-lab/PlanarReconstruction) |
| Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks | Junyi Pan et al. | ICCV | 2019 | [[PDF]](https://arxiv.org/pdf/1909.00321.pdf) [[CODE]](https://github.com/jnypan/TMNet) |
| Mesh R-CNN | Georgia Gkioxari et al. | ICCV | 2019 | [[PDF]](https://arxiv.org/pdf/1906.02739.pdf) [[CODE]](https://github.com/facebookresearch/meshrcnn) |
| **Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction** | Yiming Qian and Yasutaka Furukawa | ECCV | 2020 | [[PDF]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520324.pdf) |
### Multi-view Reconstruction
| Title | Authors | Venue | Year | Resources |
| :----------------------------------------------------------- | ------------------------- | -------------------------- | ---- | ------------------------------------------------------------ |
| **Reconstructing piecewise planar scenes with multi-view regularization** | Weijie Xi and Xuejin Chen | Computational Visual Media | 2019 | [[PDF]](https://link.springer.com/content/pdf/10.1007/s41095-019-0159-7.pdf) |
| PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo | Jiachen Liu et al. | CVPR| 2022 | [[PDF]](https://arxiv.org/pdf/2203.12082.pdf) |
### Generative / Implicit Field
| Title | Authors | Venue | Year | Resources |
| :----------------------------------------------------------- | ------------------------- | ----- | ---- | ------------------------------------------------------------ |
| Continuous Signed Distance Functions for 3D Vision | Simen Haugo et al. | 3DV | 2017 | [[PDF]](https://lightbits.github.io/papers/haugo2017continuous.pdf) |
| AtlasNet: A Papier-Mâché Approach to Learning 3D Surface | Thibault Groueix et al. | CVPR | 2018 | [[PDF]](https://arxiv.org/pdf/1802.05384.pdf) [[CODE]](https://github.com/ThibaultGROUEIX/AtlasNet) |
| DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation | Jeong Joon Park et al. | CVPR | 2019 | [[PDF]](https://arxiv.org/pdf/1901.05103.pdf) [[CODE]](https://github.com/facebookresearch/DeepSDF) |
| Occupancy Networks: Learning 3D Reconstruction in Function Space | Lars Mescheder et al. | CVPR | 2019 | [[PDF]](https://arxiv.org/pdf/1812.03828) [[CODE]](https://github.com/autonomousvision/occupancy_networks) |
| Learning Implicit Fields for Generative Shape Modeling | Zhiqin Chen and Hao Zhang | CVPR | 2019 | [[PDF]](https://arxiv.org/pdf/1812.02822.pdf) [[CODE]](https://github.com/czq142857/implicit-decoder) |
| Learning Shape Templates with Structured Implicit Functions | Kyle Genova et al. | ICCV | 2019 | [[PDF]](https://arxiv.org/pdf/1904.06447.pdf) |
| **BSP-Net: Generating Compact Meshes via Binary Space Partitioning** | Zhiqin Chen et al. | CVPR | 2020 | [[PDF]](https://arxiv.org/pdf/1911.06971.pdf) [[CODE]](https://github.com/czq142857/BSP-NET-pytorch) |
| Points2Surf: Learning Implicit Surfaces from Point Clouds | Philipp Erler et al. | ECCV | 2020 | [[PDF]](https://arxiv.org/pdf/2007.10453.pdf) [[CODE]](https://github.com/ErlerPhilipp/points2surf) |
| Overfit Neural Networks as a Compact Shape Representation | Thomas Davies et al. | arXiv | 2020 | [[PDF]](https://arxiv.org/pdf/2009.09808.pdf) |
| Implicit Geometric Regularization for Learning Shapes | Amos Gropp et al. | ICML | 2020 | [[PDF]](https://arxiv.org/pdf/2002.10099.pdf) [[CODE]](https://github.com/amosgropp/IGR) |
| Deep Parametric Shape Predictions using Distance Fields | Dmitriy Smirnov et al. | CVPR | 2020 | [[PDF]](https://arxiv.org/pdf/1904.08921.pdf) [[CODE]](https://github.com/dmsm/DeepParametricShapes) |
| **PolyGen: An Autoregressive Generative Model of 3D Meshes** | Charlie Nash et al. | arXiv | 2020 | [[PDF]](https://arxiv.org/pdf/2002.10880v1.pdf) |
| **CvxNet: Learnable Convex Decomposition** | Boyang Deng et al. | arXiv | 2020 | [[PDF]](https://arxiv.org/pdf/1909.05736.pdf) [[CODE]](https://github.com/tensorflow/graphics/tree/master/tensorflow_graphics/projects/cvxnet) |
| Local Deep Implicit Functions for 3D Shape | Kyle Genova et al. | CVPR | 2020 | [[PDF]](https://ldif.cs.princeton.edu/assets/paper.pdf) [[CODE]](https://github.com/google/ldif) |
| Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling | Omid Poursaeed et al. | ECCV | 2020 | [[PDF]](https://arxiv.org/pdf/2007.10294.pdf) |
| CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly | Fenggen Yu et al. | CVPR | 2022 | [[PDF]](https://arxiv.org/pdf/2104.05652.pdf) |
| Neural 3D Scene Reconstruction with the Manhattan-world Assumption | Haoyu Guo et al. | CVPR | 2022 | [[PAGE]](https://zju3dv.github.io/manhattan_sdf) |
| NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors | Jiepeng Wang et al. | ECCV | 2022 | [[PAGE]](https://jiepengwang.github.io/NeuRIS/) |
### Kinetic Reconstruction
| Title | Authors | Venue | Year | Resources |
| :---------------------------------------------------------- | ----------------------------------------- | -------- | ---- | ------------------------------------------------------------ |
| KIPPI: KInetic Polygonal Partitioning of Images | JEAN-PHILIPPE BAUCHET and FLORENT LAFARGE | CVPR | 2018 | [[PDF]](https://hal.inria.fr/hal-01740958/file/kippi.pdf) [[CODE]](https://www-sop.inria.fr/members/Florent.Lafarge/code/kippi.rar) |
| Approximating shapes in images with low-complexity polygons | Muxingzi Li et al. | CVPR | 2020 | [[PDF]](https://hal.inria.fr/hal-02526028/document) [[CODE]](https://www-sop.inria.fr/members/Florent.Lafarge/code/ASIP.zip) |
| **Kinetic Shape Reconstruction** | JEAN-PHILIPPE BAUCHET and FLORENT LAFARGE | SIGGRAPH | 2020 | [[PDF]](https://hal.inria.fr/hal-02924409/document) |
### Mesh Deformation
| Title | Authors | Venue | Year | Resources |
| :----------------------------------------------------------- | ----------------------------------- | -------- | ---- | ------------------------------------------------------------ |
| **Point2Mesh: A Self-Prior for Deformable Meshes** | RANA HANOCKA et al. | SIGGRAPH | 2020 | [[PDF]](https://arxiv.org/pdf/2005.11084.pdf) [[CODE]](https://github.com/ranahanocka/Point2Mesh/) |
| ShapeFlow: Learnable Deformations Among 3D Shapes | Chiyu Jiang et al. | NeurIPS | 2020 | [[PDF]](https://arxiv.org/abs/2006.07982) [[CODE]](https://github.com/maxjiang93/ShapeFlow) |
| Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows | Kunal Gupta and Manmohan Chandraker | NeurIPS | 2020 | [[PDF]](https://arxiv.org/pdf/2007.10973.pdf) [[CODE]](https://github.com/KunalMGupta/NeuralMeshFlow) |
| Learning Deformable Tetrahedral Meshes for 3D Reconstruction | Jun Gao et al. | NeurIPS | 2020 | [[PDF]](https://arxiv.org/abs/2011.01437) [[CODE]](https://github.com/nv-tlabs/DefTet) |
### Constructive / Parametric Geometry
| Title | Authors | Venue | Year | Resources |
| :----------------------------------------------------------- | ----------------------------------------- | ---------------------- | ---- | ------------------------------------------------------------ |
| Boolean operations on 3D selective Nef complexes: Data structure, algorithms, optimized implementation and experiments | Peter Hachenberger et al. | Computational Geometry | 2007 | [[PDF]](https://www.sciencedirect.com/science/article/pii/S0925772107000181) [[CODE]](https://doc.cgal.org/latest/Nef_3/index.html) |
| Mesh arrangements for solid geometry | Qingnan Zhou et al. | SIGGRAPH | 2016 | [[PDF]](https://dl.acm.org/doi/pdf/10.1145/2897824.2925901) [[CODE]](https://libigl.github.io/tutorial/#boolean-operations-on-meshes) |
| 3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks | Chuhang Zou et al. | ICCV | 2017 | [[PDF]](https://openaccess.thecvf.com/content_ICCV_2017/papers/Zou_3D-PRNN_Generating_Shape_ICCV_2017_paper.pdf) [[CODE]](https://github.com/zouchuhang/3D-PRNN) |
| CSGNet: Neural Shape Parser for Constructive Solid Geometry | Gopal Sharma et al. | CVPR | 2018 | [[PDF]](https://arxiv.org/pdf/1712.08290.pdf) |
| **Surface Reconstruction from 3D Line Segments** | Pierre-Alain Langlois et al. | 3DV | 2019 | [[PDF]](https://arxiv.org/pdf/1911.00451.pdf) [[CODE]](https://github.com/palanglois/line-surface-reconstruction) |
| ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds | Gopal Sharma et al. | ECCV | 2020 | [[PDF]](https://arxiv.org/pdf/2003.12181.pdf) [[CODE]](https://github.com/Hippogriff/parsenet-codebase) |
| PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes arXiv:1911.10949v3 | Rundi Wu et al. | CVPR | 2020 | [[PDF]](https://arxiv.org/pdf/1911.10949.pdf) [[CODE]](https://github.com/ChrisWu1997/PQ-NET) |
| **UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree** | Kacper Kania et al. | NIPS | 2020 | [[PDF]](https://arxiv.org/pdf/2006.09102v3.pdf) [[CODE]](https://github.com/kacperkan/ucsgnet) |
| State of the Art on Computational Design of Assemblies with Rigid Parts | Ziqi Wang et al. | EUROGRAPHICS | 2021 | [[PDF]](https://kiki007.github.io/publication/assemblysurvey/paper.pdf) |
| Reconstruction of Convex Polytope Compositions from 3D Point-clouds | Markus Friedrich and Pierre-Alain Fayolle | GRAPP | 2021 | [[PDF]](https://www.scitepress.org/Papers/2021/102971/102971.pdf) [[CODE]](https://github.com/mafried/csg_playground/tree/fullpipeline_polytope) |
### Triangulation
| Title | Authors | Venue | Year | Resources |
| :-------------------------------------------------- | ---------------------------------- | ----- | ---- | ------------------------------------------------------------ |
| PointTriNet: Learned Triangulation of 3D Point Sets | Nicholas Sharp and Maks Ovsjanikov | ECCV | 2020 | [[PDF]](https://nmwsharp.com/media/papers/learned-triangulation/learned_triangulation.pdf) [[CODE]](https://github.com/nmwsharp/learned-triangulation) |
### Wireframe
| Title | Authors | Venue | Year | Resources |
| :-------------------------------------------------- | ---------------------------------- | ----- | ---- | ------------------------------------------------------------ |
| PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds | Yujia Liu et al. | ICLR | 2021 | [[PDF]](https://openreview.net/pdf?id=8X2eaSZxTP) |
### Urban Reconstruction
| Title | Authors | Venue | Year | Resources |
| :----------------------------------------------------------- | --------------------- | -------------- | ---- | ------------------------------------------------------------ |
| LOD Generation for Urban Scenes | Yannick Verdie et al. | ACM TOG | 2015 | [[PDF]](https://hal.inria.fr/hal-01113078/file/manuscript.pdf) |
| Manhattan-world Urban Reconstruction from Point Clouds | Minglei Li et al. | ECCV | 2016 | [[PDF]](https://3d.bk.tudelft.nl/liangliang/publications/2016/manhattan/manhattan_eccv2016.pdf) |
| **Connect-and-Slice: an hybrid approach for reconstructing 3D objects** | Hao Fang et al. | CVPR | 2020 | [[PDF]](https://hal.inria.fr/hal-02541349/document) |
| **Relation-Constrained Automatic 3D Reconstruction of Buildings in Metropolitan Areas from Photogrammetric Point Clouds** | Yuan Li and Bo Wu | Remote Sensing | 2021 | [[PDF]](https://www.mdpi.com/2072-4292/13/1/129/pdf) |
| **Combined Rule-Based and Hypothesis-Based Method for Building Model Reconstruction from Photogrammetric Point Clouds** | Linfu Xie et al. | Remote Sensing | 2021 | [[PDF]](https://www.researchgate.net/publication/350065884_Combined_Rule-Based_and_Hypothesis-Based_Method_for_Building_Model_Reconstruction_from_Photogrammetric_Point_Clouds) |
| **Reconstructing Compact Building Models from Point Clouds Using Deep Implicit Fields** | Zhaiyu Chen et al. | ISPRS | 2022 | [[PDF]](https://www.sciencedirect.com/science/article/pii/S0924271622002611) [[CODE]](https://github.com/chenzhaiyu/points2poly) |
| **Point2Roof: End-to-end 3D Building Roof Modeling from Airborne LiDAR Point Clouds** | Li Li et al. | ISPRS | 2022 | [[PDF]](https://www.sciencedirect.com/science/article/abs/pii/S0924271622002362) [[CODE]](https://github.com/Li-Li-Whu/Point2Roof) |
## License
[](https://creativecommons.org/licenses/by/4.0/)
This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).