MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence | Paper Citation Record

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This paper cites Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Classification Problem Solving

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence , title =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence New Ways to Make Microcircuits Smaller---Duplicate Entry

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Clancey and Glenn Rennels , abstract =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence and Rennels, Glenn R

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Poligon: A System for Parallel Problem Solving

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Transfer of Rule-Based Expertise through a Tutorial Dialogue

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence The Engineering of Qualitative Models

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2017 , eprint=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Pluto: The 'Other' Red Planet

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Unresolved cited work

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Journal of Foo , volume = 14, number = 1, pages =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Unresolved cited work

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Computer Graphics Forum , volume =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Unresolved cited work

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence and Helmberg, Christoph , TITLE =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Applied Mathematics and Computation , volume=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Scientific Reports , volume=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence doi:10.5281/zenodo.1003157 , url =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Kobbelt and M

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Lafortune and Sing-Choong Foo and Kenneth E

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Unresolved cited work

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence IEEE Transactions on Visualization and Computer Graphics , year=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Proceedings of the ACM Workshop on 3D Object Retrieval , pages =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2020 , publisher=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence The Visual Computer , volume=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Science , volume=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Unresolved cited work

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence A novel binary shape context for

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Harmonic mean normalized

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2007 , publisher=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Eurographics , title =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2000 , //publisher =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Computer Graphics Forum , volume =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence and Van Kaick, O

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence and Pottmann, Helmut , title =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence ACM Transactions on Graphics , volume=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence European Conference on Computer Vision Workshops , author =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Learning shape correspondence with anisotropic convolutional neural networks , volume =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Advances in Neural Information Processing Systems , volume=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence DAGM German Conference on Pattern Recognition , pages=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence and Bronstein, A

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence ACM Transactions on Graphics , volume=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Proceedings of the ACM Workshop on 3D Object Retrieval , pages=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence ACM Transactions on Graphics , volume=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence and Rodolà, E

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Computer graphics forum , volume=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence and Chambolle, A

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence A dimensional reduction guiding deep learning architecture for

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Learned binary spectral shape descriptor for

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence and Bronstein, A

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Spectral Generalized Multi-Dimensional Scaling , year =

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Non-rigid Shape Correspondence Using Surface Descriptors and Metric Structures in the Spectral Domain

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Computer Graphics Forum , volume=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence International Conference on Learning Representations (ICLR) , year=

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence GSPBOX: A toolbox for signal processing on graphs

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2021 , pages =

Reference 64

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Observation bd2b3c5c-9fe4-40d1-a63a-836a519b663f · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2011 , issn =

Reference 65

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Observation dbeaa897-85d1-4e44-897f-43c3d93677b2 · outbound

This paper cites Computer Graphics Forum , volume=.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Computer Graphics Forum , volume=

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:37.454903Z

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Observation e435530d-82e2-4c76-b501-eef403ec6ccd · outbound

This paper cites Computer-Aided Design , volume=.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Computer-Aided Design , volume=

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:37.524654Z

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Observation 5667db0c-bfd5-4f52-87b2-5b5b8616d853 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 1988 , publisher=

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:37.618822Z

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Observation a716f89f-0d8d-42d6-9824-72a7d54cc7b2 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2017 , issn =

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:37.715436Z

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Observation b3a248c3-6bc6-49f0-9edd-f761de14545a · outbound

This paper cites and Masci, J.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence and Masci, J

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:37.777956Z

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Observation da1c6b96-27ad-479a-bdd7-0df0db21b6fe · outbound

This paper cites 2020 , pages=.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2020 , pages=

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:37.843791Z

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Observation a52afa2d-30ab-45ae-97c8-b92583c37076 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Generalizable local feature pre-training for deformable shape analysis , booktitle=

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:37.952201Z

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Observation 520aca0d-1b2a-4b0c-a38c-36d2b7370857 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Understanding and improving features learned in deep functional maps , booktitle=

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:38.072739Z

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Observation dba5ae45-7db1-431a-997b-6fea1a8a599e · outbound

This paper cites 2021 , issn =.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2021 , issn =

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:38.236296Z

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Observation f3b31595-a82c-4592-95f2-05a97c45c887 · outbound

This paper cites The Average Mixing Kernel Signature , booktitle =.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence The Average Mixing Kernel Signature , booktitle =

Reference 75

Resolution: verified exact; doi, observed 2026-08-01T22:13:37.580361Z

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Observation eecd9c65-7966-4a29-b44b-6cfeb644ef5b · outbound

This paper cites The effect of spatial information characterization on.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence The effect of spatial information characterization on

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:38.487208Z

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Observation 60c11b70-2609-42b6-b037-dcfd86485af7 · outbound

This paper cites Evaluating Local Geometric Feature Representations for.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Evaluating Local Geometric Feature Representations for

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:38.607737Z

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Observation 1e06d029-9383-419b-8f44-cd1739880788 · outbound

This paper cites Intrinsic and Extrinsic Operators for Shape Analysis , booktitle =.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Intrinsic and Extrinsic Operators for Shape Analysis , booktitle =

Reference 78

Resolution: verified exact; doi, observed 2026-08-01T22:13:37.401820Z

Source-reported events for the cited work

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Observation 7311e582-abc2-406f-a2d0-6b1ae23126a9 · outbound

This paper cites Stable topological signatures for points on.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Stable topological signatures for points on

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:38.824286Z

Source-reported events for the cited work

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Observation 2d444bb2-9731-4a69-a8ed-3814e4721444 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:38.934745Z

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Observation 521ab4ea-f7ae-42a5-b7b8-75bdb0d29edb · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence A comprehensive performance evaluation of

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:38.999899Z

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Observation c4ff9a0b-443d-42f9-af90-e0e65cdd0ae5 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence IEEE Transactions on Visualization and Computer Graphics , volume=

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:39.082409Z

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Observation 0f15c03a-bd43-43cf-9c47-b4f147b16bea · outbound

This paper cites Using spin images for efficient object recognition in cluttered.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Using spin images for efficient object recognition in cluttered

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:39.168695Z

Source-reported events for the cited work

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Observation 1c324c11-dc07-4595-90b0-4ce18d1e2b60 · outbound

This paper cites Computer Graphics Forum , volume=.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Computer Graphics Forum , volume=

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:39.276002Z

Source-reported events for the cited work

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Observation b17def06-4bab-47ea-b7f6-14888efb9716 · outbound

This paper cites Symposium on Geometry Processing , volume=.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Symposium on Geometry Processing , volume=

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:39.446879Z

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Observation 8fc302f0-1026-4ffe-a91b-dda941cedaba · outbound

This paper cites A survey on data-driven.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence A survey on data-driven

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:39.547052Z

Source-reported events for the cited work

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Observation 29e19972-82d2-4288-a032-d6044d1a79ba · outbound

This paper cites Graphical Models , volume=.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Graphical Models , volume=

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:39.605441Z

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Observation 5e469189-bab0-43c3-8adc-e8f8eb6f8638 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2023 , publisher=

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:39.705334Z

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Observation da764f77-6b6c-40aa-8719-f28b3904e60b · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2010 , publisher=

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:39.813002Z

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Observation be0f60c9-4edd-4845-b5d9-8defc737922b · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence The Visual Computer , volume=

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:39.874304Z

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Observation 8b140b35-1777-475b-bc48-2e45719dc460 · outbound

This paper cites Computer Graphics Forum , title =.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Computer Graphics Forum , title =

Reference 91

Resolution: verified exact; doi, observed 2026-08-01T22:13:37.189882Z

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Observation 28fa8548-2a9c-4506-b902-21dce6bf4522 · outbound

This paper cites ACM Transactions on Graphics , pages =.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence ACM Transactions on Graphics , pages =

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:40.012029Z

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Observation 04be112b-0fa1-4833-9846-ff24a236d927 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence 2019 , booktitle =

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:40.129442Z

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Observation 365b8daa-7299-4bfc-b0e0-e40309e9bd4e · outbound

This paper cites The Visual Computer , volume=.

MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence The Visual Computer , volume=

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:40.246655Z

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Observation aaf8fbc9-9020-4220-a5ea-49ca1ae4ec2a · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Unresolved cited work

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:40.349415Z

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Observation 8bc652d3-e914-44e1-a6d0-e04f026510bf · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence The graph windowed

Reference 96

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Observation dfc210db-d754-48dc-b740-39a3802c2f37 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Proceedings of International Conference on 3D Vision , doi =

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:40.508603Z

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Observation ef9868f3-3ddf-4f16-82c5-091fa48ac285 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition , title =

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:40.635616Z

Source-reported events for the cited work

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Observation f2b7fae1-4e76-4382-8e03-1bdf0a8897f2 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence and Lipman, Yaron and Funkhouser, Thomas , journal =

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:40.782239Z

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Observation c42d7988-7e0c-47d9-a564-f5c3e397f099 · outbound

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MDND: Unsupervised Learning Guided by Non-Differentiable Refinement for Shape Correspondence Computing and Processing Correspondences with Functional Maps , year =

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-01T22:09:40.934513Z

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Outbound references

Pith citing papers