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Paper Citation Record · LEDGER

Level-Set Parameters: Novel Representation for 3D Shape Analysis

As of 18 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 0 inbound Pith citation observations for arXiv:2412.13502.

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

pith.paper-citation-record.v1
2412.13502 v2

Coverage vector

measured 81 of 81 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:08:07.491975Z

measured 81 of 81 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

81 of 81 outbound references displayed

  • verified exact0
  • verified fuzzy58
  • unresolved23
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1ecb3bda-2781-4a3d-ad96-52d10da6626f · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 1

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Observation acbbb937-3bbc-489c-a9c6-b1a4308b3b23 · outbound

This paper cites Dynamic graph cnn for learning on point clouds.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Dynamic graph cnn for learning on point clouds

Reference 2

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Observation dae8364e-afb9-4eb3-81ca-b758d23a5dc7 · outbound

This paper cites PointCNN: Convolution on x-transformed points.

Level-Set Parameters: Novel Representation for 3D Shape Analysis PointCNN: Convolution on x-transformed points

Reference 3

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Observation 49239577-e2f2-4f2f-b88f-dbc09dc86d8b · outbound

This paper cites Spherical kernel for efficient graph convolution on 3d point clouds.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Spherical kernel for efficient graph convolution on 3d point clouds

Reference 4

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Observation 290117be-32eb-4f27-911a-8e14a83b124c · outbound

This paper cites MeshCNN: a network with an edge.

Level-Set Parameters: Novel Representation for 3D Shape Analysis MeshCNN: a network with an edge

Reference 5

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 00d21579-44e2-4487-9a9a-508287f0abb4 · outbound

This paper cites Subdivision-based mesh convolution networks.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Subdivision-based mesh convolution networks

Reference 6

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Source-reported events for the cited work

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Observation 8d971406-8bdf-4223-bc9c-7877a6ae8dae · outbound

This paper cites Deepsdf: Learning continuous signed distance functions for shape representation.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Deepsdf: Learning continuous signed distance functions for shape representation

Reference 7

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Observation f60864c0-a6a5-4147-9927-b6d64fd7b9d0 · outbound

This paper cites Implicit neural representations with periodic activation functions.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Implicit neural representations with periodic activation functions

Reference 8

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Observation f5955df0-25f4-4c93-8899-d61de2e93c35 · outbound

This paper cites Neural fields in visual computing and beyond.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Neural fields in visual computing and beyond

Reference 9

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Observation eedd3fe7-8663-40c1-99fa-1831f3865941 · outbound

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

Level-Set Parameters: Novel Representation for 3D Shape Analysis Occupancy networks: Learning 3d reconstruction in function space

Reference 10

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Observation 3357bef1-bd60-4482-87ae-3a2b08d1ba52 · outbound

This paper cites Points2surf learning implicit surfaces from point clouds.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Points2surf learning implicit surfaces from point clouds

Reference 11

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation de5fdb06-bbe4-4a5c-96be-a412ba6061f0 · outbound

This paper cites Learning implicit fields for generative shape modeling.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Learning implicit fields for generative shape modeling

Reference 12

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Observation 3a776063-c5a6-48a5-acc6-39c978124bf4 · outbound

This paper cites Convolutional occupancy networks.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Convolutional occupancy networks

Reference 13

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Observation 48f12bdb-cc2a-4059-8f4f-bfac9bef7ba0 · outbound

This paper cites Implicit functions in feature space for 3d shape reconstruction and completion.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Implicit functions in feature space for 3d shape reconstruction and completion

Reference 14

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 96d69cbc-9922-498d-afd7-cb11e4ea5dfa · outbound

This paper cites MetaSDF: Meta- learning signed distance functions.

Level-Set Parameters: Novel Representation for 3D Shape Analysis MetaSDF: Meta- learning signed distance functions

Reference 15

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 2a8a8fcf-5da1-427d-8661-14b660f0d177 · outbound

This paper cites Hyperdiffusion: Generating im- plicit neural fields with weight-space diffusion.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Hyperdiffusion: Generating im- plicit neural fields with weight-space diffusion

Reference 16

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Observation 614a3039-423d-4994-946d-054f9a638abb · outbound

This paper cites Deep learning on 3D neural fields.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Deep learning on 3D neural fields

Reference 17

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 4a0f3ce5-1abd-4009-9e20-0921526acbac · outbound

This paper cites Vector neurons: A general framework for so (3)-equivariant networks.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Vector neurons: A general framework for so (3)-equivariant networks

Reference 18

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Observation c501e348-52e4-4821-bc87-aa969481535d · outbound

This paper cites Learning so (3) equivariant representations with spherical cnns.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Learning so (3) equivariant representations with spherical cnns

Reference 19

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Observation f6724272-88b6-417d-aebd-982284292bb3 · outbound

This paper cites Spherical CNNs.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Spherical CNNs

Reference 20

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Source-reported events for the cited work

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Observation 9c3a35cc-6247-4771-846e-ce9b83f70333 · outbound

This paper cites Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Reference 21

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Observation cf685faa-05f2-4f0a-9aca-2a318c5f6034 · outbound

This paper cites A functional approach to rotation equivariant non-linearities for tensor field networks.

Level-Set Parameters: Novel Representation for 3D Shape Analysis A functional approach to rotation equivariant non-linearities for tensor field networks

Reference 22

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Observation fe94b556-b3eb-4b53-a0ca-fd9849567272 · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Level-Set Parameters: Novel Representation for 3D Shape Analysis ShapeNet: An Information-Rich 3D Model Repository

Reference 23

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Observation 95020be5-0678-43c2-8196-c4ca3e191d83 · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes.

Level-Set Parameters: Novel Representation for 3D Shape Analysis 3d shapenets: A deep representation for volumetric shapes

Reference 24

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Observation 002ef6ab-41be-44ac-956d-47e8d44fc1a6 · outbound

This paper cites Learning-based point cloud registration for 6d object pose estimation in the real world.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Learning-based point cloud registration for 6d object pose estimation in the real world

Reference 25

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Observation ab065138-a704-44ee-91b7-a927780e702d · outbound

This paper cites Escape from cells: Deep kd-networks for the recognition of 3d point cloud models.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Escape from cells: Deep kd-networks for the recognition of 3d point cloud models

Reference 26

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Observation a8f74ad9-bade-429c-b91e-c40af8d4b537 · outbound

This paper cites PointNet++: Deep hierarchical feature learning on point sets in a metric space.

Level-Set Parameters: Novel Representation for 3D Shape Analysis PointNet++: Deep hierarchical feature learning on point sets in a metric space

Reference 27

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation cc7219c3-c959-4bec-b9f5-c4fd3dd99f79 · outbound

This paper cites Graph attention convolution for point cloud semantic segmentation.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Graph attention convolution for point cloud semantic segmentation

Reference 28

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Observation 48bae2a5-2dc5-43b2-88bd-17490d88d9af · outbound

This paper cites Pointconv: Deep convolutional networks on 3d point clouds.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Pointconv: Deep convolutional networks on 3d point clouds

Reference 29

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Observation 25e8626d-0b8f-439f-a920-fafbfbb0dbf9 · outbound

This paper cites Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J

Reference 30

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 4ea31227-90b7-401a-b0c4-cce7f2a8b398 · outbound

This paper cites Point transformer.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Point transformer

Reference 31

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Observation 7f570f2b-005b-48a2-8528-ebd838ae103b · outbound

This paper cites PCT: Point cloud transformer.

Level-Set Parameters: Novel Representation for 3D Shape Analysis PCT: Point cloud transformer

Reference 32

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raw_fallback, observed 2026-08-11T13:08:07.953134Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 7fd7a118-c790-466d-8767-eaf6fa0d7eba · outbound

This paper cites Point transformer V2: Grouped vector attention and partition-based pooling.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Point transformer V2: Grouped vector attention and partition-based pooling

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation eee1e289-1a25-4ef3-b9e2-6a97d3eba440 · outbound

This paper cites V oxnet: A 3d convolutional neural network for real-time object recognition.

Level-Set Parameters: Novel Representation for 3D Shape Analysis V oxnet: A 3d convolutional neural network for real-time object recognition

Reference 34

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Unavailable: canonical work link unavailable.

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Observation 44ebda71-5328-4b51-9bda-a48cd4e02454 · outbound

This paper cites Octnet: Learning deep 3d representations at high resolutions.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Octnet: Learning deep 3d representations at high resolutions

Reference 35

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raw_fallback, observed 2026-08-11T13:08:07.934069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3c18e127-a19f-431e-948d-a5aa09e1dfb1 · outbound

This paper cites 3d semantic segmentation with submanifold sparse convolutional networks.

Level-Set Parameters: Novel Representation for 3D Shape Analysis 3d semantic segmentation with submanifold sparse convolutional networks

Reference 36

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raw_fallback, observed 2026-08-11T13:08:07.927815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9d36a823-72eb-4786-bca2-326b85c3d82c · outbound

This paper cites Hodgenet: Learning spectral geometry on triangle meshes.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Hodgenet: Learning spectral geometry on triangle meshes

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.920838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.395637Z digest=sha256:b6337bd7eed60aed43b4584149261abdfc747bbe517d90facb5c2b157b5114b2

Observation a6668186-0290-4074-b0dc-8f14b2aa3966 · outbound

This paper cites Mesh convolution with continuous filters for 3-d surface parsing.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Mesh convolution with continuous filters for 3-d surface parsing

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.914611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.397500Z digest=sha256:fd16feaf949ba109ccb47ecca010f994e58c684ec2afc47b469a0dcf16975200

Observation f858b65a-a69e-4afd-aafe-f4eedeb6f728 · outbound

This paper cites Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T13:08:07.399299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:08:07.399299Z digest=sha256:105b22deeb3135255d1783b481a40082458aa8f69a46ac6695aa10abae85581f

Observation b9cd8d6d-f9b6-4ffb-b836-fa0fc0f97993 · outbound

This paper cites Pose estimation for augmented reality: a hands-on survey.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Pose estimation for augmented reality: a hands-on survey

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.906845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.401405Z digest=sha256:c66743b1e186795a4f2f2bda1d889a3ff9b65e61b41ea026c8cea8e3729e1274

Observation a5bfa230-5dc5-460b-96aa-4da6c69c068c · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T13:08:07.403256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:08:07.403256Z digest=sha256:d36e6a52ee7e94193655c133a39f6941842f160c77784c0534b45997a63b14ca

Observation 6ffe102d-bbf4-4f18-8bb7-6463b267ca8c · outbound

This paper cites Densefusion: 6d object pose estimation by iterative dense fusion.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Densefusion: 6d object pose estimation by iterative dense fusion

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.801328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.404980Z digest=sha256:77daaae0e964e99a94cac540c779713cdd36e99a92b9ae9687ba4880cc184c4d

Observation 980e65df-ef7a-49c4-a78b-4a730f16239e · outbound

This paper cites Normal- ized object coordinate space for category-level 6d object pose and size estimation.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Normal- ized object coordinate space for category-level 6d object pose and size estimation

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.795540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.406860Z digest=sha256:2780e2794bc29f188016394b8375a1e60dc3473f96276749a82169897f43d8fe

Observation 7379bcf6-b762-4455-9a0a-805c9756b64d · outbound

This paper cites PVnet: Pixel-wise voting network for 6dof pose estimation.

Level-Set Parameters: Novel Representation for 3D Shape Analysis PVnet: Pixel-wise voting network for 6dof pose estimation

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.788354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.409152Z digest=sha256:fefc90e048d2940723b1269a7209fdfc1d93d028b0676d14c956fb00520c33f0

Observation ad8f0c92-d6cd-480a-b189-b4b9df64fa17 · outbound

This paper cites Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.781714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.411128Z digest=sha256:10154aada11785f3a50ee0a6b9b018fe3b44b1f00bb0e18d764aee105b51f0ff

Observation 91dd6ad6-fead-4696-bc22-48c7aedc7d89 · outbound

This paper cites Center-based decoupled point-cloud registration for 6d object pose estimation.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Center-based decoupled point-cloud registration for 6d object pose estimation

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.775861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.413139Z digest=sha256:ff2d7c216b8007405deebbd2d62efe67e229323857f666cf63f25e1c608d9555

Observation 080363e5-439f-4d26-a30e-0ab8b25a4ee8 · outbound

This paper cites 3dmatch: Learning local geometric descriptors from rgb-d reconstructions.

Level-Set Parameters: Novel Representation for 3D Shape Analysis 3dmatch: Learning local geometric descriptors from rgb-d reconstructions

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.769052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.415587Z digest=sha256:06996b6060cdb874f03372b0a6500252b62340098a5e5eec4f45be2f7999e90f

Observation 1cc61e08-7ebb-47c7-bdca-09587e68e69c · outbound

This paper cites Fully convolutional geometric features.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Fully convolutional geometric features

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.762480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.418087Z digest=sha256:783395b4dda140bc95aef3e84ae067c6ad1ac4aae4d83846f0395339c841f24f

Observation e46e6d0b-de59-4ef7-84e9-36a29f349483 · outbound

This paper cites Deep closest point: Learning representations for point cloud registration.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Deep closest point: Learning representations for point cloud registration

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.756841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.420451Z digest=sha256:8600f090628adbf0e19a6efff71a88aedff0381112cdfb1acfd1214564e8bdd3

Observation d49d8497-0d7f-4008-9e5f-1a411c6ba641 · outbound

This paper cites Predator: Registration of 3d point clouds with low overlap.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Predator: Registration of 3d point clouds with low overlap

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.750297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.422422Z digest=sha256:332ecc4ddc7c1d5cde42e1d833e379c6d8580143ff7848446c535c0f8f757e4f

Observation ce0d262e-fcea-490b-8810-c4bda4ff3636 · outbound

This paper cites Buffer: Balancing accuracy, efficiency, and generalizability in point cloud registration.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Buffer: Balancing accuracy, efficiency, and generalizability in point cloud registration

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.743276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.425757Z digest=sha256:12322bb6acae624f8dc9ee72f570cee175c18f976910ffe2352ccbc692edd385

Observation d1331848-ded5-41ce-b337-1d258aeeed4f · outbound

This paper cites Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.735174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.428197Z digest=sha256:3418ba941c1b2c5f71d7c0da30dd0a53c666dcc7dff4a57bd5f12b84a5000c6d

Observation fd0f8f98-020b-4102-bede-f13be9176e0f · outbound

This paper cites Pointnetlk: Robust & efficient point cloud registration using pointnet.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Pointnetlk: Robust & efficient point cloud registration using pointnet

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.728356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.430425Z digest=sha256:1cb764e26c6940e9a8cccaf2a9d97db1318009db85fb20b8a377ec41330a4d54

Observation c9732fa7-deb0-4056-9c36-85aba6675189 · outbound

This paper cites Method for registration of 3-d shapes.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Method for registration of 3-d shapes

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.720257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.433199Z digest=sha256:7971b74efc4c8de23fafaa6651aabe114e600497144633a8cd232a6d597d8409

Observation 13e979d0-55ce-4db3-8069-67851045eaff · outbound

This paper cites Fast global registration.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Fast global registration

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.712707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.435898Z digest=sha256:4531274d3560a45269515325a1f4e1075a1c393e777b174644564caa2f42db30

Observation 85c5a932-04cd-4eb9-8fe8-4e3d304be6da · outbound

This paper cites Teaser: Fast and certifiable point cloud registration.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Teaser: Fast and certifiable point cloud registration

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.706597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.438383Z digest=sha256:fb22a6b2b40ae44fb88cf0a52314daedb91051d954916411bed2342e932191d0

Observation cb4c4c34-2cc4-4bd5-97c7-c6fef44afd19 · outbound

This paper cites Go-icp: A globally optimal solution to 3d icp point-set registration.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Go-icp: A globally optimal solution to 3d icp point-set registration

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.699398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.440398Z digest=sha256:9c795184a882c49c5b071fb3d8088943ee0c9500ac32b2063954cddeacb9defb

Observation 023ea954-efb9-4f9f-a34f-f4ffcdd67cc9 · outbound

This paper cites SAL: Sign agnostic learning of shapes from raw data.

Level-Set Parameters: Novel Representation for 3D Shape Analysis SAL: Sign agnostic learning of shapes from raw data

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.691330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.442275Z digest=sha256:c00ee8218a6156529547110fef3d9b1c023b3f0f157cd576bbdd3b8f7ebc1ece

Observation 3e3953ab-9fd1-448b-b761-c76da427010c · outbound

This paper cites Implicit geometric regularization for learning shapes.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Implicit geometric regularization for learning shapes

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.684781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.444110Z digest=sha256:9b2028ba121842f615a993fd6e8899563ebcba4320f75b2b7c9f65badf1e3e5b

Observation a01e0c06-1aec-4865-a3dd-157c66318ed2 · outbound

This paper cites Nerf in the wild: Neural radiance fields for unconstrained photo collections.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Nerf in the wild: Neural radiance fields for unconstrained photo collections

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.678365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.446023Z digest=sha256:4e1d7b87d812fd0ed4049decf5d46841b6f55cafb3aae31acda90ba9b0236b10

Observation 3bf9a049-1604-460a-9ddb-50cb78dcc2b7 · outbound

This paper cites Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.670791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.447764Z digest=sha256:c2bafcf5dbdf21692e1f496dc1099188c8afb8039aeecf44f2cb8bec39bd4b53

Observation 187b396f-8dbc-460b-a9bc-6d8f368c8480 · outbound

This paper cites MonoSDF: Exploring monocular geometric cues for neural implicit surface reconstruction.

Level-Set Parameters: Novel Representation for 3D Shape Analysis MonoSDF: Exploring monocular geometric cues for neural implicit surface reconstruction

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.664525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.449876Z digest=sha256:bfd27188fde5a64baeb96e3e4b9e65e34a9d8b571e88df2f421864223a1b3662

Observation df6f9d75-bf26-4a0c-ba53-b1265f2e5636 · outbound

This paper cites V olume rendering of neural implicit surfaces.

Level-Set Parameters: Novel Representation for 3D Shape Analysis V olume rendering of neural implicit surfaces

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.657504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.451879Z digest=sha256:0b7f9ee07dfddb50029c442bbe2aeddff746549e9710f1076565d7cc34ae35ce

Observation 888ed19a-d0b7-45b3-bee7-d016a334e97d · outbound

This paper cites Digs: Divergence guided shape implicit neural representation for unoriented point clouds.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Digs: Divergence guided shape implicit neural representation for unoriented point clouds

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.650124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.453638Z digest=sha256:3b583613cb5252cd286abf0a01a776b10511afe04f56c7a49ca05cb389b24cf7

Observation 3e5dfe48-fdee-4691-9d6e-23d1ce306c50 · outbound

This paper cites From data to functa: Your data point is a function and you can treat it like one.

Level-Set Parameters: Novel Representation for 3D Shape Analysis From data to functa: Your data point is a function and you can treat it like one

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-11T13:08:07.455912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:08:07.455912Z digest=sha256:d5f0df2b07cb07b85bd07442f6fd842df9efcbc63bf8d4a92968607bea3ce1b3

Observation 0df38ce4-f8f1-4034-92bf-c77f90345d2b · outbound

This paper cites Modulated periodic activations for generalizable local functional representations.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Modulated periodic activations for generalizable local functional representations

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.642876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.458374Z digest=sha256:342672195e4c8e332362d93d8c338c38b237bc1a902672be05903666d22bbb3f

Observation fede554b-188f-498f-9ee9-c6e9fff50a83 · outbound

This paper cites pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis.

Level-Set Parameters: Novel Representation for 3D Shape Analysis pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.634505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.460229Z digest=sha256:286b7c8d74cb71b64d74fda2007bd7071bb7feaa53a5301ca6203909e6e54f86

Observation d8c855bb-e70b-4c6d-998f-09bb7ec5790e · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Model-agnostic meta-learning for fast adaptation of deep networks

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-11T13:08:07.462129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:08:07.462129Z digest=sha256:0b71e2afaf5f22ff3f8418bcaad56c6c420d2410f3f79f88a242ee4edd58d5e6

Observation 3e111ff0-0a6d-4b32-a267-65c411e0a14d · outbound

This paper cites Learned initializations for optimizing coordinate-based neural representations.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Learned initializations for optimizing coordinate-based neural representations

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.621272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.464984Z digest=sha256:d01bfeaedfd103817dd97bfd19e56d528773d4c1a0d1cb736ddc429cb9d0f8f2

Observation 7c3d0d9a-56b6-48c4-857a-418bd71fce10 · outbound

This paper cites An introduction to variational autoencoders.

Level-Set Parameters: Novel Representation for 3D Shape Analysis An introduction to variational autoencoders

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.613872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.467480Z digest=sha256:d97431aa7819b06fe7ce826e6f076e678596d5bb2b637c9bae0d0b68b231ee43

Observation a666f32e-64e8-4afa-a839-846d1abecbe9 · outbound

This paper cites Gensdf: Two-stage learning of generalizable signed distance functions.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Gensdf: Two-stage learning of generalizable signed distance functions

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.607045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.469453Z digest=sha256:8f2a8eab63ec15e406837be591eff38a19315fb4464d1b44c15f6442f5089e9a

Observation 9c4a1d7e-47f5-46aa-a3d9-4f19c3b9a3d8 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-11T13:08:07.471147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:08:07.471147Z digest=sha256:d5dae30c1e2353e9803e7a5791563257e98e263920ef6293963ced82446dd5e4

Observation a6cc7380-4556-41b0-84a2-14120066a74a · outbound

This paper cites Srinivasan, Matthew Tancik, Jonathan T.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Srinivasan, Matthew Tancik, Jonathan T

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.595463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.473611Z digest=sha256:b9ad1b76930c1c85d5e058a55bb97dd7b48552f0e8f0c929ef39f5daf0cb5b51

Observation 9973fd2a-50ab-4573-8b60-bf094a24c6eb · outbound

This paper cites Visualizing data using t-sne.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Visualizing data using t-sne

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-11T13:08:07.476363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:08:07.476363Z digest=sha256:894dc5c2ac1fa160040ac0b52dc20a38c0778dd7319492d9cc2718af5cbea58e

Observation 1c1b737c-9eda-4f25-9c22-7719c2763188 · outbound

This paper cites Direct visibility of point sets.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Direct visibility of point sets

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.583941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.478600Z digest=sha256:8a8cf33d13c8a9b1212308666f31dcb9975c4fcc01861225254efd04bb27809c

Observation 3e3f7421-1d75-44e9-980d-bcc36c601c4d · outbound

This paper cites Spatial Functa: Scaling Functa to ImageNet Classification and Generation.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Spatial Functa: Scaling Functa to ImageNet Classification and Generation

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-11T13:08:07.480588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:08:07.480588Z digest=sha256:6f2ffa8b722a5afa25af7ccf501fa08467712d087a7a3222b9538a65260c31db

Observation 2cd7a999-b7ec-462c-adea-a1932f221cdc · outbound

This paper cites Generative neural fields by mixtures of neural implicit functions.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Generative neural fields by mixtures of neural implicit functions

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.576799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.483405Z digest=sha256:7c4d462e08cf3e9ef4f9516a5187581a678fd2d2d8ffda7c5fd1413895c8fcee

Observation e255293a-b626-429c-b65a-24f5b7fe095a · outbound

This paper cites Machine learning: a probabilistic perspective.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Machine learning: a probabilistic perspective

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-11T13:08:07.485498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:08:07.485498Z digest=sha256:4b1dc9666e34c3c887e15dfb8cc35029ce1866a05e4553fb76a29b5b18e787f8

Observation 0f466634-54b4-4e7c-b1bf-2417b56b81dd · outbound

This paper cites Deep neural networks as gaussian processes.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Deep neural networks as gaussian processes

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.565637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.487695Z digest=sha256:55deaa36dccd45ef8c840816573c680899bbb625d9f0b714adafc970024e7c3e

Observation 3c0af478-df6b-469f-b00b-7b536d6988b4 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Adam: A Method for Stochastic Optimization

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-11T13:08:07.489747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:08:07.489747Z digest=sha256:66965e1a5c0089d4b738028528f0530289e892da5d229edcd64f209da6857308

Observation cd30cfc1-190f-4ef4-9766-26a7db341fb3 · outbound

This paper cites Rotation and translation invariant representation learning with implicit neural representations.

Level-Set Parameters: Novel Representation for 3D Shape Analysis Rotation and translation invariant representation learning with implicit neural representations

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:08:07.557212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T13:08:07.491975Z digest=sha256:5d6311dfe8ef1f0bf8ad96765830e6dc26f5224754e17cd981fc4fd6a7d996e6

Pith citing papers

No inbound Pith citation observations are available.