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Paper Citation Record · LEDGER

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition

As of 8 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2506.21165.

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

pith.paper-citation-record.v1
2506.21165 v1

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:40:16.369675Z

measured 73 of 73 standing notices

One-hop event checks from named stored sources.

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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

73 of 73 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cc0b7c27-84ac-4b2a-b15d-9bbeea60e60b · outbound

This paper cites Pointgl: a simple global-local framework for efficient point cloud analysis,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pointgl: a simple global-local framework for efficient point cloud analysis,

Reference 1

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Observation 548b32f2-2069-4bcd-8f24-5d6992ea73de · outbound

This paper cites Domain adaptive lidar point cloud segmentation with 3d spatial consistency,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Domain adaptive lidar point cloud segmentation with 3d spatial consistency,

Reference 2

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Observation 844a5363-631d-4b01-ae6d-b50ca8a52a65 · outbound

This paper cites Cmnet: Component-aware matching network for few-shot point cloud classification,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Cmnet: Component-aware matching network for few-shot point cloud classification,

Reference 3

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Observation bb10c673-4be9-4d44-a259-954f2ab0f463 · outbound

This paper cites Geometric back-projection net- work for point cloud classification,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Geometric back-projection net- work for point cloud classification,

Reference 4

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Source-reported events for the cited work

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Observation 9e8f50b3-60c0-4ce3-8f76-9db7ac8f3f95 · outbound

This paper cites Cattrack: Single-stage category-level 6d object pose tracking via convolution and vision trans- former,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Cattrack: Single-stage category-level 6d object pose tracking via convolution and vision trans- former,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 7bee216c-90b6-431f-81a0-7167bc6a5cea · outbound

This paper cites Real-time 3d single object tracking with transformer,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Real-time 3d single object tracking with transformer,

Reference 6

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Source-reported events for the cited work

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Observation f2210454-2157-4014-a1d8-bb9a2a52c95d · outbound

This paper cites Vpfnet: Improving 3d object detection with virtual point based lidar and stereo data fusion,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Vpfnet: Improving 3d object detection with virtual point based lidar and stereo data fusion,

Reference 7

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Observation 66ad901d-c96d-43f5-946c-46c1acec2962 · outbound

This paper cites Centertube: Tracking multiple 3d objects with 4d tubelets in dynamic point clouds,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Centertube: Tracking multiple 3d objects with 4d tubelets in dynamic point clouds,

Reference 8

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Source-reported events for the cited work

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Observation d3be67cf-5e6a-40a8-bdea-b114cc83216d · outbound

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

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition ShapeNet: An Information-Rich 3D Model Repository

Reference 9

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Source-reported events for the cited work

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Observation 61b1fa94-cdf2-496a-8022-3c57ecfc7755 · outbound

This paper cites Modelnet: Towards a datacenter emulation environment,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Modelnet: Towards a datacenter emulation environment,

Reference 10

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Observation eb980a1f-bea8-4150-9d01-3d06f5f65274 · outbound

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

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pointnet: Deep learning on point sets for 3d classification and segmentation,

Reference 11

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Observation dabcbed9-9736-4c00-9181-d59976e995d0 · outbound

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

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

Reference 12

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Source-reported events for the cited work

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Observation f15ba3f5-6371-4cea-8ac0-aea0a120ca9c · outbound

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

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Dynamic graph cnn for learning on point clouds,

Reference 13

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Source-reported events for the cited work

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Observation cbd67f1e-1662-4400-a4e1-e036370cf165 · outbound

This paper cites Kpconv: Flexible and deformable convolution for point clouds,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Kpconv: Flexible and deformable convolution for point clouds,

Reference 14

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verified fuzzy
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Source-reported events for the cited work

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Observation 2a5a90bc-7605-478c-a27d-b7975fbc9aa6 · outbound

This paper cites Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling,

Reference 15

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Observation 4da61464-6856-45c8-900c-0f2d5716a3b3 · outbound

This paper cites Pct: Point cloud transformer,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pct: Point cloud transformer,

Reference 16

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Observation 0005035a-2985-405e-aeb9-3170207f8ca0 · outbound

This paper cites V oxelnet: End-to-end learning for point cloud based 3d object detection,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition V oxelnet: End-to-end learning for point cloud based 3d object detection,

Reference 17

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Observation e8c84856-a2ba-4fdb-b7ee-f234bf7b8aad · outbound

This paper cites Modeling point clouds with self-attention and gumbel subset sampling,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Modeling point clouds with self-attention and gumbel subset sampling,

Reference 18

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Source-reported events for the cited work

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Observation bb30d762-f2fd-4683-b683-0d3ae4f401b2 · outbound

This paper cites So-net: Self-organizing network for point cloud analysis,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition So-net: Self-organizing network for point cloud analysis,

Reference 19

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Source-reported events for the cited work

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Observation ae0a7c5b-a044-4c20-97f3-4e831071371a · outbound

This paper cites Spherical CNNs.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Spherical CNNs

Reference 20

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Source-reported events for the cited work

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Observation 1a860c81-fa7e-4aaa-b1cb-3283dfb1f0f1 · outbound

This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Scannet: Richly-annotated 3d reconstructions of indoor scenes,

Reference 21

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Observation 384498bb-715c-4b21-899e-3488001b8df9 · outbound

This paper cites Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data,

Reference 22

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Observation 4eff495e-9870-415e-98c9-a54186734718 · outbound

This paper cites Classification of Single-View Object Point Clouds.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Classification of Single-View Object Point Clouds

Reference 23

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Observation e5d17a86-8a73-481c-b08d-0c528607eb3b · outbound

This paper cites Transferable representation learning with deep adaptation networks,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Transferable representation learning with deep adaptation networks,

Reference 24

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Observation 7165b06c-f5ef-4df2-9ae3-d941ce9e284d · outbound

This paper cites Contrastive adaptation network for unsupervised domain adaptation,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Contrastive adaptation network for unsupervised domain adaptation,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 0fcc04d0-68dd-4c32-9099-141876112885 · outbound

This paper cites Domain-adversarial training of neural networks,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Domain-adversarial training of neural networks,

Reference 26

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Source-reported events for the cited work

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Observation b50c610e-b6c3-4d1e-b05c-93b6a3a8bfff · outbound

This paper cites Maximum classi- fier discrepancy for unsupervised domain adaptation,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Maximum classi- fier discrepancy for unsupervised domain adaptation,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9b879fb1-cac9-48fe-be00-afde7368554c · outbound

This paper cites Learning semantic represen- tations for unsupervised domain adaptation,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Learning semantic represen- tations for unsupervised domain adaptation,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 95b74172-be41-4653-8a2a-f6fc90f5b69e · outbound

This paper cites Transferrable prototypical networks for unsupervised domain adaptation,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Transferrable prototypical networks for unsupervised domain adaptation,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 1a15455e-95c1-4e33-9621-7aefd4a85e71 · outbound

This paper cites Pointdan: A multi- scale 3d domain adaption network for point cloud representation,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pointdan: A multi- scale 3d domain adaption network for point cloud representation,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 27fb54b7-0027-431a-890c-01407a4211ba · outbound

This paper cites Self-supervised learning for domain adaptation on point clouds,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Self-supervised learning for domain adaptation on point clouds,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation df525966-e617-4838-bd76-68fcdaf1c3e3 · outbound

This paper cites Geometry-aware self-training for unsupervised domain adaptation on object point clouds,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Geometry-aware self-training for unsupervised domain adaptation on object point clouds,

Reference 32

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raw_fallback, observed 2026-08-06T22:40:22.436984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9525e514-da8b-4f3c-ac1c-837dae9cc8c3 · outbound

This paper cites Domain adaptation on point clouds via geometry-aware implicits,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Domain adaptation on point clouds via geometry-aware implicits,

Reference 33

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raw_fallback, observed 2026-08-06T22:40:22.267484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.026862Z digest=sha256:1930146f213004f69afb3352e37e4bbc23b5d024f97ccc8a52473302e9594cfd

Observation ed6d7315-9e2e-44cc-90be-acf0309b739b · outbound

This paper cites Self-supervised global-local structure modeling for point cloud domain adaptation with reliable voted pseudo labels,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Self-supervised global-local structure modeling for point cloud domain adaptation with reliable voted pseudo labels,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:22.075327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.099098Z digest=sha256:f021429df0cda05d83c1848c72c94d1159af6a33083b092df4633e0c5bd7b513

Observation 84983c97-33c7-4af9-8efd-2fac2de57d30 · outbound

This paper cites Quasi-balanced self- training on noise-aware synthesis of object point clouds for closing domain gap,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Quasi-balanced self- training on noise-aware synthesis of object point clouds for closing domain gap,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:21.915221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.149123Z digest=sha256:6c1dcf32decbb9634a014afb3fd9739b2009d9df2e48fc276a96ef1c7533336b

Observation 2d53fa71-f09b-4d2e-b673-91afe2290038 · outbound

This paper cites Learning generalizable part-based feature representation for 3d point clouds,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Learning generalizable part-based feature representation for 3d point clouds,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:21.731981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.200809Z digest=sha256:f49c413ffccaf8f7fc677c7d9a48f6ad6d9109977bfbefb544606dce01e14c17

Observation a8af48e3-039b-48a6-9821-83b1cc86e47f · outbound

This paper cites Deep convolutional networks do not classify based on global object shape,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Deep convolutional networks do not classify based on global object shape,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:21.567162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.238721Z digest=sha256:74df599af7d68b005eb0018f95eef43386be9c6fe78166a5b01d68b415d9500b

Observation 253ab114-b20c-4c9c-be84-783b94696cce · outbound

This paper cites ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T22:40:14.278706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:40:14.278706Z digest=sha256:56622ced62df251b3fe5acebb1b561c00e9021d2bdd290108154aa536ce8558f

Observation 133ce065-794f-43cc-a6c8-1a1a47c6471f · outbound

This paper cites Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:21.368734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.337717Z digest=sha256:f9c6725336b63c7f63e9a51fd68e28462877d3149f0258ae397525661a63b769

Observation 91c519a3-4cfc-4dec-b0d3-7b5467fb23f2 · outbound

This paper cites Parts of recognition,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Parts of recognition,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:21.169977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.420325Z digest=sha256:fc2803c18cab2f10401781f1ad195ae4414ed35fe5105dec809f20d614d31797

Observation e6602d9d-56ba-4ec3-aad3-6394e29d4109 · outbound

This paper cites Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis

Reference 41

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no resolver link, observed 2026-08-06T22:40:14.471404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:40:14.471404Z digest=sha256:421858eb0bc0bd74b2effac889895f9ebd4a62cfc07bf472a8d8b3700a8b5f13

Observation 0bfa8477-cb82-4e4a-a2ca-194690098a8e · outbound

This paper cites Reconstructing continuous distributions of 3D protein structure from cryo-EM images.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Reconstructing continuous distributions of 3D protein structure from cryo-EM images

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T22:40:14.523425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:40:14.523425Z digest=sha256:c1633314615c81d4da41c7550ce6493d1f666cdd494ba67025df5070b6d38340

Observation 48aef62d-cc77-4c83-b23a-f5342ffd822b · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view synthesis,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Nerf: Representing scenes as neural radiance fields for view synthesis,

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T22:40:14.570218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:40:14.570218Z digest=sha256:88f3e84fe7b54b320a1a4e934073875c321bd18515005747778aeab9ce2a521a

Observation 5f45571a-d887-4ce3-829c-120c9259af2f · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Fourier features let networks learn high frequency functions in low dimensional domains,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:20.930210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.619254Z digest=sha256:892ba1e68a52870555a44c5c2fa81366bfd7364e4155de26aaad6941817d97a4

Observation 013a3643-7f1a-4ce8-8c39-f9ee54a14e21 · outbound

This paper cites Metasets: Meta- learning on point sets for generalizable representations,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Metasets: Meta- learning on point sets for generalizable representations,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:20.792390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.675319Z digest=sha256:9dfd899046b5af6cdd0f0576ba1ea23af6f6cfb9dc98373fcc9e3a9f842458d9

Observation 7c7c5b4f-aca5-4c62-a383-da5f35ca0b70 · outbound

This paper cites Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors

Reference 46

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.758173Z digest=sha256:fd51bdd41f8ec1feea654d45b4bdfac6ca67d9c47648591018ba984faf937c5e

Observation b25f1d6f-850d-4d7d-9164-8c121d0a3a51 · outbound

This paper cites Pointcnn: Convolution on x-transformed points,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pointcnn: Convolution on x-transformed points,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:20.593645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.807825Z digest=sha256:98f38d885f88449df86ae17eeb2d6e85a725f555b7d154c00b4be6d14f5f5b31

Observation ec5c60ae-86d1-4b36-91d9-e0d2e21304e0 · outbound

This paper cites Clusternet: Deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Clusternet: Deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:20.395240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.853814Z digest=sha256:b7656b826a20caf3be97dcc477af08744b5f5f22099637690668b95d84dfe30c

Observation 3e3ca94a-88a2-4cd5-81cd-bddf4bd31286 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition mixup: Beyond Empirical Risk Minimization

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T22:40:14.909044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:40:14.909044Z digest=sha256:6ba9e0e19f21892397e6dffbedef2dadd1775ddf5b377355cff12809c1736f18

Observation f5489cba-c095-4d68-b793-198e1f889379 · outbound

This paper cites Manifold mixup: Better representations by interpolating hidden states,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Manifold mixup: Better representations by interpolating hidden states,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:20.217442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:14.965948Z digest=sha256:728dc7f8b2bca02f89ec5d2c99203037cdef352232a0a905b0527dd4277adb84

Observation b020861c-9a2d-4eed-a984-5ff0c138668e · outbound

This paper cites Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:20.049257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.025901Z digest=sha256:44a8c49b118bd0fa7ec14de4f33feb3288acb3082fcab7a63a8ae4bd11e5ec14

Observation 912478f7-fbcf-4a39-8cb0-71b240f05f38 · outbound

This paper cites Puzzle mix: Exploiting saliency and local statistics for optimal mixup,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Puzzle mix: Exploiting saliency and local statistics for optimal mixup,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:19.888006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.104926Z digest=sha256:651be357b5865fa14590b9ca518f49ca50e97546ab5e219e9a5a3d269c4fb900

Observation 64e4ab9e-3f30-4ad9-849c-799499c23eff · outbound

This paper cites Pointmixup: Augmentation for point clouds,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pointmixup: Augmentation for point clouds,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:19.643789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.177808Z digest=sha256:cdd9aa2f9797b43dd37275d7acdc37cb6c2cd1d72f9c60557f8347d64b54a7c6

Observation a491bb12-79d1-4957-a57c-00e100e8abf3 · outbound

This paper cites Pointaugment: an auto-augmentation framework for point cloud classification,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pointaugment: an auto-augmentation framework for point cloud classification,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:19.334002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.236115Z digest=sha256:cbd841abe2c4e66819170cfb780bcfdfe967e2863498893815df41c1f3ba6dee

Observation df171739-d884-4d19-bbe5-83127cf27260 · outbound

This paper cites Pointcutmix: Regularization strategy for point cloud classification,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pointcutmix: Regularization strategy for point cloud classification,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:19.107631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.300523Z digest=sha256:fab7b344844364cece770e121df6f86c77aabea8bb3269502b52ab14e9aac325

Observation dd5c24c0-86f6-4aab-83a6-4f2527ea3b58 · outbound

This paper cites Interpolation consistency training for semi-supervised learning,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Interpolation consistency training for semi-supervised learning,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:18.883187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.305430Z digest=sha256:7693d26d4d68891d33848c9fa181e5280fd1607a4cf60457afed6ccd5e046555

Observation 516df53f-650b-42bb-9995-142cc52831f4 · outbound

This paper cites Vision GNN: An Image is Worth Graph of Nodes.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Vision GNN: An Image is Worth Graph of Nodes

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T22:40:15.339584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:40:15.339584Z digest=sha256:9a933bb23606f121dc85cb273debd33a3f5a87bd6bc4d9a289ab44022690b869

Observation 5a88d55e-c80a-4c4c-acb2-e360b0a6217e · outbound

This paper cites Deepgcns: Can gcns go as deep as cnns?.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Deepgcns: Can gcns go as deep as cnns?

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:18.684625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.385089Z digest=sha256:084255a59a01ab4ed12cc50674ac960c6ac57886c3c1e2cda0c89ea8b5f98a29

Observation eff60aaa-4123-48b1-9f20-65a88d2e5618 · outbound

This paper cites Learning cross-modal contrastive features for video domain adaptation,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Learning cross-modal contrastive features for video domain adaptation,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:18.533338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.447061Z digest=sha256:5fb10a5cc7db40fb0796d667cb35941deb79c72708bba485d0ffa1f8966fbb7d

Observation e875492a-e50e-4328-a1bf-00bd2dcf6d96 · outbound

This paper cites Learning a nonlinear embedding by preserving class neighbourhood structure,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Learning a nonlinear embedding by preserving class neighbourhood structure,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:18.384090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.503108Z digest=sha256:2e51a6a86972701c3a76c1187ace214164c93f547d8e0ab54dceb7ca5c570740

Observation a183bd87-23ed-417b-b67d-8395a39aa3e9 · outbound

This paper cites Improving generalization via scalable neighborhood component analysis,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Improving generalization via scalable neighborhood component analysis,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:18.184881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.579339Z digest=sha256:d11776cced795638b729ff5e7bb3bf9165eb2125d8769d4aa8827c5bf5419e88

Observation 551a2657-1aef-4002-8ae5-dda8983cf82b · outbound

This paper cites Supervised contrastive learn- ing,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Supervised contrastive learn- ing,

Reference 62

Resolution
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no resolver link, observed 2026-08-06T22:40:15.649290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:40:15.649290Z digest=sha256:8d4f1d41f97ea63995a62a7105ba56c2d8e981589ba9e02d2013eac4e0cf49c3

Observation f4131a66-d8ce-4b1d-94ae-e06cfabf820d · outbound

This paper cites Graspnet-1billion: A large- scale benchmark for general object grasping,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Graspnet-1billion: A large- scale benchmark for general object grasping,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:17.989890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.707841Z digest=sha256:39d16d41f2051a69e452444b4e4385ee7f86f114d046e378d3a97aa6427dbb6c

Observation 9c4b82da-a00e-4d93-b0f7-3548f723b11c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Adam: A Method for Stochastic Optimization

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-06T22:40:15.746820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:40:15.746820Z digest=sha256:d85dc3591895bc256d34757e39dabcb015e1744f87b482544d9731bbcd752510

Observation 7eb5b1a8-495e-4c93-a61f-0a493e3e5d82 · outbound

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

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-06T22:40:15.802432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:40:15.802432Z digest=sha256:ac4356ff4673170662da02b8ee7293a6e520627e148c0303829a6f0fd82f52e8

Observation 2d8f9cb4-5fe8-4a93-bb7c-fdde0728ad33 · outbound

This paper cites Self-supervised deep learning on point clouds by reconstructing space,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Self-supervised deep learning on point clouds by reconstructing space,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:17.851877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.848050Z digest=sha256:46c3d0f38080df8f78480a74f0b166b7d67ee8c4cceb461b3da69e67c70602a9

Observation 96826355-0779-4c71-9c9c-a9ad065d7659 · outbound

This paper cites Self-distillation for unsupervised 3d domain adaptation,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Self-distillation for unsupervised 3d domain adaptation,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:17.663888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.920787Z digest=sha256:d7706f1f8488f0e5a9fb7306efccf51489da925855665a03dbbcfa215541048e

Observation 7f7fd202-4901-4eb8-afbd-29379fb2169e · outbound

This paper cites Domain adaptive sampling for cross- domain point cloud recognition,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Domain adaptive sampling for cross- domain point cloud recognition,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:17.496939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:15.986382Z digest=sha256:27813d6b37c59f587852562e90581449609e67967338e54dc9fc8c6fcb34f7e7

Observation 230c9dd0-544c-43fb-872e-9f3ae61a2987 · outbound

This paper cites Synergizing contrastive learning and optimal transport for 3d point cloud domain adaptation,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Synergizing contrastive learning and optimal transport for 3d point cloud domain adaptation,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:17.362673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 5b0bab5b-83c1-49c1-a287-a98f4ec3c07b · outbound

This paper cites A theory of learning from different domains,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition A theory of learning from different domains,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:17.169817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:16.139085Z digest=sha256:78d0d4a7e2e267f43f1faa482c946c28de54e9f2c53c361564c6aa958f7559e3

Observation 28549a2c-7a88-4722-82f4-9cadd45fa681 · outbound

This paper cites Domain Adaptation: Learning Bounds and Algorithms.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Domain Adaptation: Learning Bounds and Algorithms

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-06T22:40:16.219111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:40:16.219111Z digest=sha256:b3a48de0eae2f9478708a800c3dc9803f43225ae3e55bef149e911878b1ff340

Observation 6e237d6f-674c-416f-93a2-db79659c0212 · outbound

This paper cites Pointcloud saliency maps,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Pointcloud saliency maps,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:17.033554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:16.294934Z digest=sha256:a9f55e56fd8265bfa9590a99db736d3d64325d89ea036633d336e82b36a3953a

Observation 7ce22121-5c2b-4741-9d0c-1ae12d84e91d · outbound

This paper cites Visualizing data using t-sne,.

Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition Visualizing data using t-sne,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:40:16.910365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:40:16.369675Z digest=sha256:d49f64e497fc8b3f19136c05e241e17081e6aa22c1ffb48fe93e87497606afbd

Pith citing papers

No inbound Pith citation observations are available.