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Paper Citation Record · LEDGER

Deep Meta Functionals for Shape Representation

As of 14 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1908.06277.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.06277 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

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measured 37 of 37 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6d4724dd-7411-4add-a335-12914298c587 · outbound

This paper cites Learning feed-forward one-shot learners.

Deep Meta Functionals for Shape Representation Learning feed-forward one-shot learners

Reference 1

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Observation 268c3ba0-a0ef-4e60-b3bf-5173e1e772f5 · outbound

This paper cites Polygonization of implicit surfaces.

Deep Meta Functionals for Shape Representation Polygonization of implicit surfaces

Reference 2

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Observation 73007186-ea82-47bf-9197-4306da5c8cd6 · outbound

This paper cites A geometric model for active contours in image pro- cessing.

Deep Meta Functionals for Shape Representation A geometric model for active contours in image pro- cessing

Reference 3

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Observation 4d5a780b-691e-431f-a469-8fc8c9b19335 · outbound

This paper cites A geometric model for active contours in image pro- cessing.

Deep Meta Functionals for Shape Representation A geometric model for active contours in image pro- cessing

Reference 4

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Observation b9cc592a-68e3-4394-86bc-8a892cc45b0e · outbound

This paper cites Geodesic active contours.

Deep Meta Functionals for Shape Representation Geodesic active contours

Reference 5

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Observation 51253e87-e3e7-4a1f-890a-dcf38e06dcc7 · outbound

This paper cites Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Mano- lis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu.

Deep Meta Functionals for Shape Representation Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Mano- lis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu

Reference 6

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Observation d9e29dd6-a1dd-4ef2-91e9-d8e7b026d03f · outbound

This paper cites Learning implicit fields for generative shape modeling, 2018.

Deep Meta Functionals for Shape Representation Learning implicit fields for generative shape modeling, 2018

Reference 7

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Observation 36583545-8210-497a-bc77-a2413b58e4ab · outbound

This paper cites 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction.

Deep Meta Functionals for Shape Representation 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction

Reference 8

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Observation 17c87833-2887-4ac9-928e-bfab3a8ae3cf · outbound

This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

Deep Meta Functionals for Shape Representation Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Reference 9

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Deep Meta Functionals for Shape Representation Unresolved cited work

Reference 10

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Observation 6851f20e-e4d3-4a5b-986b-66ee1cb34e29 · outbound

This paper cites Kim, Bryan C.

Deep Meta Functionals for Shape Representation Kim, Bryan C

Reference 11

Resolution
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Observation 90df7d0b-04d1-4187-be34-34b1768c390c · outbound

This paper cites HyperNetworks.

Deep Meta Functionals for Shape Representation HyperNetworks

Reference 12

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Observation eb874b6f-cd13-4ace-ae40-7d90cd98826c · outbound

This paper cites Hi- erarchical surface prediction for 3d object reconstruction.

Deep Meta Functionals for Shape Representation Hi- erarchical surface prediction for 3d object reconstruction

Reference 13

Resolution
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Observation 49bd0c2e-3df1-4ec5-83b5-d1f1246819b2 · outbound

This paper cites Ray tracing algebraic surfaces.

Deep Meta Functionals for Shape Representation Ray tracing algebraic surfaces

Reference 14

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Observation db3e17d3-6df1-422d-ae2b-b887f999d9b4 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level perfor- mance on imagenet classification.

Deep Meta Functionals for Shape Representation Delving deep into rectifiers: Surpassing human-level perfor- mance on imagenet classification

Reference 15

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Observation d3630211-57c2-4895-9fac-77564dbdae5c · outbound

This paper cites Dynamic filter networks.

Deep Meta Functionals for Shape Representation Dynamic filter networks

Reference 16

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Observation c760a188-043e-4822-a856-44cf67dce23b · outbound

This paper cites Gal: Geometric adversarial loss for single-view 3d-object recon- struction.

Deep Meta Functionals for Shape Representation Gal: Geometric adversarial loss for single-view 3d-object recon- struction

Reference 17

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Observation 216bfe41-ec75-421a-a2ab-8fcc9569ec37 · outbound

This paper cites Learning a multi-view stereo machine, 2017.

Deep Meta Functionals for Shape Representation Learning a multi-view stereo machine, 2017

Reference 18

Resolution
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Observation fefc5ff8-098d-4126-9afe-1150dcf361cf · outbound

This paper cites Snakes: Active contour models.

Deep Meta Functionals for Shape Representation Snakes: Active contour models

Reference 19

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Observation 1aa790b0-1437-4816-a124-971946678224 · outbound

This paper cites Learning View Priors for Single-view 3D Reconstruction.

Deep Meta Functionals for Shape Representation Learning View Priors for Single-view 3D Reconstruction

Reference 20

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Observation e02599ad-6942-4cfd-9d2e-534751721039 · outbound

This paper cites Neu- ral 3d mesh renderer.

Deep Meta Functionals for Shape Representation Neu- ral 3d mesh renderer

Reference 21

Resolution
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Observation 074eff2d-ee9d-4c8b-9061-ea9c158f5693 · outbound

This paper cites Gradient flows and ge- ometric active contour models.

Deep Meta Functionals for Shape Representation Gradient flows and ge- ometric active contour models

Reference 22

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Observation 9f4a7670-9ba3-4428-b53c-4c5b2d4795f0 · outbound

This paper cites A dynamic convolutional layer for short range weather prediction.

Deep Meta Functionals for Shape Representation A dynamic convolutional layer for short range weather prediction

Reference 23

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Observation adc65205-3a54-4e28-8f51-3f10fc329eaf · outbound

This paper cites Kosinski.

Deep Meta Functionals for Shape Representation Kosinski

Reference 24

Resolution
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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 5b68bc01-a738-4605-9e73-0a0e41053b43 · outbound

This paper cites Active contour based segmentation of 3d surfaces.

Deep Meta Functionals for Shape Representation Active contour based segmentation of 3d surfaces

Reference 25

Resolution
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Observation 851211f5-9334-40b1-a90f-22f95aff3ffe · outbound

This paper cites Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction.

Deep Meta Functionals for Shape Representation Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction

Reference 26

Resolution
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Observation 9b2e8487-f7ba-44dc-b693-b268074f21e3 · outbound

This paper cites Lorensen and Harvey E.

Deep Meta Functionals for Shape Representation Lorensen and Harvey E

Reference 27

Resolution
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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation a90de87d-e647-447e-8681-59d495c7fe4a · outbound

This paper cites Evolu- tionary fronts for topology-independent shape modeling and recovery.

Deep Meta Functionals for Shape Representation Evolu- tionary fronts for topology-independent shape modeling and recovery

Reference 28

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation bfaf16cc-0010-4251-ac7f-62a14791fcf2 · outbound

This paper cites Occupancy networks: Learning 3d reconstruction in function space, 2018.

Deep Meta Functionals for Shape Representation Occupancy networks: Learning 3d reconstruction in function space, 2018

Reference 29

Resolution
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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 697d3dcb-3bcd-496f-a033-0ef4fa1ebfde · outbound

This paper cites On the number of linear regions of deep neural networks.

Deep Meta Functionals for Shape Representation On the number of linear regions of deep neural networks

Reference 30

Resolution
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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 5ae52b79-48e6-4036-a6a3-a0e2749defa8 · outbound

This paper cites Deepsdf: Learning con- tinuous signed distance functions for shape representation,.

Deep Meta Functionals for Shape Representation Deepsdf: Learning con- tinuous signed distance functions for shape representation,

Reference 31

Resolution
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Source-reported events for the cited work

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Observation f78e81e5-3c23-4aa2-9ec8-f862e098784b · outbound

This paper cites Matryoshka networks: Predicting 3d geometry via nested shape layers.

Deep Meta Functionals for Shape Representation Matryoshka networks: Predicting 3d geometry via nested shape layers

Reference 32

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 4af68026-0bd9-4a9f-97c1-3ecbd1556352 · outbound

This paper cites Riegler, S.

Deep Meta Functionals for Shape Representation Riegler, S

Reference 33

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation c9e3d2d8-6eb6-4805-ad4d-28a90d98e1f5 · outbound

This paper cites Octnet: Learning deep 3d representations at high resolu- tions.

Deep Meta Functionals for Shape Representation Octnet: Learning deep 3d representations at high resolu- tions

Reference 34

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 05587d0e-b316-4192-8952-86a4dd4f1332 · outbound

This paper cites Octree generating networks: Efficient convolutional archi- tectures for high-resolution 3d outputs.

Deep Meta Functionals for Shape Representation Octree generating networks: Efficient convolutional archi- tectures for high-resolution 3d outputs

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation c4a3a638-7f2d-4146-b9f3-b61f1be058ab · outbound

This paper cites Pixel2mesh: Generating 3d mesh models from single rgb images.

Deep Meta Functionals for Shape Representation Pixel2mesh: Generating 3d mesh models from single rgb images

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-14T12:56:08.468205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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This paper cites Inferring point clouds from single monocular images by depth intermedia- tion, 2018.

Deep Meta Functionals for Shape Representation Inferring point clouds from single monocular images by depth intermedia- tion, 2018

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