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teaching:ss2024:3dsm [2024/04/30 14:42]
Maolin Gao |
teaching:ss2024:3dsm [2024/10/22 12:04] (current)
Viktoria Ehm |
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| ^Title ^Additional Info ^ Supervisor ^ Assigned to^ Presentation Date^ | ^Title ^Additional Info ^ Supervisor ^ Assigned to^ Presentation Date^ |
| | [[ http://www.lix.polytechnique.fr/~maks/papers/DiffusionNet_final.pdf| DiffusionNet: Discretization Agnostic Learning on Surfaces ]] | |Maolin Gao| ZH | 23.05.2024| | | [[ https://arxiv.org/pdf/2307.05663| Objaverse-XL: A Universe of 10M+ 3D Objects ]] | [[https://objaverse.allenai.org/| Webpage ]] | | | | |
| | [[ https://haggaim.github.io/projects/point_registration/PMSDP_final_light.pdf| Point Cloud Registration Via Convex Relaxation ]] | [[https://github.com/Haggaim/PM-SDP| Git Repository ]]|Maolin Gao | | | [[ https://arxiv.org/pdf/2210.11463| Breaking Bad: A Dataset for Geometric Fracture and Reassembly ]] | [[https://breaking-bad-dataset.github.io/| Webpage ]] | | | | |
| | [[https://arxiv.org/abs/1801.07829| Dynamic Graph CNN for Learning on Point Clouds ]] | [[https://github.com/WangYueFt/dgcnn| Git Repository ]]|Maolin Gao| JS| 02.05.2024| | | [[ https://arxiv.org/pdf/2106.13679| Shape registration in the time of transformers ]] | [[https://github.com/GiovanniTRA/transmatching| Webpage ]] | | | | |
| | [[https://cvg.cit.tum.de/_media/spezial/bib/schmidt-et-al-14.pdf| Dense Elastic 3D Shape Matching ]] | [[https://cvg.cit.tum.de/_media/spezial/bib/cremers-dcurv14.pdf| Extended Literature 1 ]] [[https://projet.liris.cnrs.fr/imagine/pub/proceedings/SGP-2011/pdf/v30i5pp1471-1480.pdf| Extended Literature 2 ]]| Viktoria Ehm | | | [[ https://www.lix.polytechnique.fr/~maks/papers/3DOR2024_3D_foundation_retrieval.pdf| Fine-tuning 3D foundation models for geometric object retrieval ]] | | | | | |
| | [[http://www.lix.polytechnique.fr/~maks/papers/obsbg_fmaps.pdf| Functional Maps: A Flexible Representation of Maps Between Shapes ]] | |Viktoria Ehm | JG | 25.04.2024 | | | [[ https://openaccess.thecvf.com/content/CVPR2024/papers/Cao_Spectral_Meets_Spatial_Harmonising_3D_Shape_Matching_and_Interpolation_CVPR_2024_paper.pdf| Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation ]] | [[https://github.com/dongliangcao/Spectral-Meets-Spatial |
| | [[https://arxiv.org/pdf/2304.14419.pdf| Unsupervised Learning of Robust Spectral Shape Matching]] | [[https://github.com/dongliangcao/Unsupervised-Learning-of-Robust-Spectral-Shape-Matching| Git Repository ]]|Viktoria Ehm | | | Webpage ]] | | | | |
| | [[https://arxiv.org/abs/2104.12229| Vector Neurons: A General Framework for SO(3)]]| |Maolin Gao| SC | 16.05.2024 | | | [[ https://arxiv.org/pdf/2403.19919| Diff-Reg: Diffusion Model in Doubly Stochastic Matrix Space for Registration Problem ]] | [[https://github.com/wuqianliang/Diff-Reg |
| | [[https://arxiv.org/pdf/1612.00593.pdf| PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation]] | |Maolin Gao|MR| 25.04.2024 | | | Webpage ]] | | | | |
| | [[https://openaccess.thecvf.com/content/ICCV2023/papers/Heinrich_Chasing_Clouds_Differentiable_Volumetric_Rasterisation_of_Point_Clouds_as_a_ICCV_2023_paper.pdf| Chasing clouds: Differentiable volumetric rasterisation of point clouds as a highly efficient and accurate loss for large-scale deformable 3D registration]] | |Maolin Gao| | | [[ https://www.dagm-gcpr.de/fileadmin/dagm-gcpr/pictures/2023_Heidelberg/Paper_MainTrack/029.pdf| A Network Analysis for Correspondence Learning via Linearly-Embedded Functions]] | | | | | |
| |[[https://arxiv.org/pdf/1601.06070.pdf| Efficient 2D-to-3D Deformable Shape Matching for 3D Shape Retrieval Applications]] | |Viktoria Ehm | | | [[ https://geometry.cs.ucl.ac.uk/group_website/projects/2024/nssm/paper_docs/paper.pdf| Neural Semantic Surface Maps ]] | [[https://geometry.cs.ucl.ac.uk/group_website/projects/2024/nssm/ |
| |[[https://arxiv.org/pdf/2110.09994.pdf| Dpfm: Deep partial functional maps]] | [[https://github.com/pvnieo/DPFM| Git Repository ]]|Viktoria Ehm| PW | 02.05.2024| | | Webpage ]] | | | | |
| |[[https://openaccess.thecvf.com/content/CVPR2021/papers/Eisenberger_NeuroMorph_Unsupervised_Shape_Interpolation_and_Correspondence_in_One_Go_CVPR_2021_paper.pdf| Neuromorph: Unsupervised shape interpolation and correspondence in one go.]] | |Viktoria Ehm | AA| 23.05.2024 | | | [[ https://frank-r-schmidt.de/Publications/2011/WSSC11/WSSC-iccv11.pdf| Geometrically Consistent Elastic Matching of 3D Shapes: A Linear Programming Solution]] | | | | | |
| | | [[ https://openaccess.thecvf.com/content/CVPR2024/papers/Roetzer_SpiderMatch_3D_Shape_Matching_with_Global_Optimality_and_Geometric_Consistency_CVPR_2024_paper.pdf| SpiderMatch: 3D Shape Matching with Global Optimality and Geometric Consistency ]] | [[https://dongliangcao.github.io/urssm/ |
| | | Webpage ]] | | | | |
| | | [[ https://arxiv.org/pdf/2304.14419| Unsupervised Learning of Robust Spectral Shape Matching ]] | [[https://paulroetzer.github.io/publications/2024-06-19-spidermatch.html |
| | | Webpage ]] | | | | |
| | | [[ https://arxiv.org/pdf/1904.07865| Zoomout: Spectral upsampling for efficient shape correspondence]] | | | | | |
| | | [[ https://arxiv.org/pdf/2312.14024| NICP: Neural ICP for 3D Human Registration at Scale]] | [[https://neural-icp.github.io/ |
| | | Webpage ]]| | | | |
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