Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Added papers using point cloud data for registration #35

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Added papers using point cloud data for registration #35

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 12 additions & 2 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,9 @@ Statistics: :fire: code is available & stars >= 100  |  :star: citatio
- [[3DV](http://segcloud.stanford.edu/segcloud_2017.pdf)] SEGCloud: Semantic Segmentation of 3D Point Clouds. [[project](http://segcloud.stanford.edu/)] [__`seg.`__ __`aut.`__] :star:
-
- [[TPAMI](https://ieeexplore.ieee.org/ielx7/34/8454009/08046026.pdf?tp=&arnumber=8046026&isnumber=8454009&ref=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8=)] Structure-aware Data Consolidation. [__`oth.`__]
- [[ICCV](https://ieeexplore.ieee.org/document/8237364)] Local-to-Global Point Cloud Registration Using a Dictionary of Viewpoint Descriptors. [**`reg.`**]
- [[[ICCV](https://ieeexplore.ieee.org/document/8237553)] Point Set Registration with Global-Local Correspondence and Transformation Estimation. [**`reg.`**]
- [[[AAAI](http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14188)] Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization. [**`reg.`**]

---
## 2018
Expand Down Expand Up @@ -437,7 +440,8 @@ Statistics: :fire: code is available & stars >= 100  |  :star: citatio
- [[CVPR](https://arxiv.org/pdf/1907.02545.pdf)] Attentive Context Normalization for Robust Permutation-Equivariant Learning. [[code](https://github.com/vcg-uvic/acne)] [__`cls.`__]
- [[CVPR](https://arxiv.org/pdf/2003.01456.pdf)] Implicit Functions in Feature Space for Shape Reconstruction and Completion. [[code](https://github.com/jchibane/if-net)] [__`oth.`__]
- [[CVPR](https://arxiv.org/pdf/2002.10876.pdf)] PointAugment: an Auto-Augmentation Framework for Point Cloud Classification. [__`cls.`__]
-
- [[CVPR](https://openaccess.thecvf.com/content_CVPR_2020/html/Pais_3DRegNet_A_Deep_Neural_Network_for_3D_Point_Registration_CVPR_2020_paper.html)] 3DRegNet: A Deep Neural Network for 3D Point Registration. [**`reg.`**]
- [[[CVPR](https://openaccess.thecvf.com/content_CVPR_2020/html/Iglesias_Global_Optimality_for_Point_Set_Registration_Using_Semidefinite_Programming_CVPR_2020_paper.html)] Global Optimality for Point Set Registration Using Semidefinite Programming. [**`reg.`**]
- [[WACV](https://arxiv.org/pdf/1912.08487.pdf)] FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data. [__`seg.`__ __`aut.`__]
-
- [[arXiv](https://arxiv.org/abs/2001.10692)] ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes. [__`det.`__]
Expand All @@ -446,7 +450,7 @@ Statistics: :fire: code is available & stars >= 100  |  :star: citatio
- [[ECCV](https://arxiv.org/pdf/2007.10985.pdf)] PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding. [__`cls.`__ __`seg.`__ __`det.`__]
- [[ECCV](https://arxiv.org/abs/2003.10826)] DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares. [[code](https://github.com/sitzikbs/DeepFit)] [__`oth.`__]
- [[ECCV](https://arxiv.org/abs/2004.11784v2)] DPDist: Comparing Point Clouds Using Deep Point Cloud Distance. [[code](https://github.com/dahliau/DPDist)] [__`oth.`__]
-
- [[[ECCV](https://link.springer.com/chapter/10.1007/978-3-030-58586-0_23)] Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. [**`reg.`**]
- [[IROS](https://hal.inria.fr/hal-02927350/document)] GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous Vehicles. [[code](https://github.com/anshulpaigwar/GndNet)] [__`seg.`__ __`aut.`__]
-
- [[ICLR](https://arxiv.org/pdf/2002.00118.pdf)] AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing. [[code](https://github.com/xingzhehe/AdvectiveNet-An-Eulerian-Lagrangian-Fluidic-Reservoir-for-Point-Cloud-Processing)][__`cls.`__ __`seg.`__]
Expand All @@ -461,6 +465,12 @@ Statistics: :fire: code is available & stars >= 100  |  :star: citatio
- [[ICRA](https://arxiv.org/abs/2105.07647)] FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection. [[code](https://github.com/weiyithu/FGR)][__`det.`__ __`seg.`__]
-
- [[ICCV](https://openaccess.thecvf.com/content/ICCV2021/papers/Hamdi_MVTN_Multi-View_Transformation_Network_for_3D_Shape_Recognition_ICCV_2021_paper.pdf)] MVTN: Multi-View Transformation Network for 3D Shape Recognition. [[code](https://github.com/ajhamdi/MVTN)][__`det.`__ __`rel.`__]
- [[CVPR](https://openaccess.thecvf.com/content/CVPR2021/html/Ali_RPSRNet_End-to-End_Trainable_Rigid_Point_Set_Registration_Network_Using_Barnes-Hut_CVPR_2021_paper.html)] RPSRNet: End-to-End Trainable Rigid Point Set Registration Network Using Barnes-Hut 2D-Tree Representation [**`reg.`**]
- [[[TPAMI](https://doi.org/10.1109/TPAMI.2020.3043769)] Acceleration of Non-Rigid Point Set Registration With Downsampling and Gaussian Process Regression. [**`reg.`**]
- [[[TPAMI](https://doi.org/10.1109/TPAMI.2020.2978477)] Point Set Registration for 3D Range Scans Using Fuzzy Cluster-Based Metric and Efficient Global Optimization. [**`reg.`**]
- [[[TPAMI](https://doi.org/10.1109/TPAMI.2019.2940655)] Topology-Aware Non-Rigid Point Cloud Registration. [**`reg.`**]
- [[[NeurIPS](https://proceedings.neurips.cc/paper/2021/hash/c85b2ea9a678e74fdc8bafe5d0707c31-Abstract.html)] CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration. [**`reg.`**]
- [[[NeurIPS](https://proceedings.neurips.cc/paper/2021/hash/2b0f658cbffd284984fb11d90254081f-Abstract.html)] Accurate Point Cloud Registration with Robust Optimal Transport. [**`reg.`**]

<h1>

Expand Down