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Paper Citation Record · LEDGER

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation

As of 8 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2506.03512.

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

pith.paper-citation-record.v1
2506.03512 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:08:14.190813Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

52 of 52 outbound references displayed

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  • verified fuzzy39
  • unresolved9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 23bcaa47-51e5-4b3b-8caa-4369079444af · outbound

This paper cites Asynchronous frameless event-based optical flow.Neural Networks, 27:32–37, 2012.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Asynchronous frameless event-based optical flow.Neural Networks, 27:32–37, 2012

Reference 1

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Observation 802762af-0d6f-4ddd-9d68-310726e95149 · outbound

This paper cites Real- time optical flow for vehicular perception with low-and high- resolution event cameras.IEEE Transactions on Intelligent Transportation Systems, 23(9):15066–15078, 2021.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Real- time optical flow for vehicular perception with low-and high- resolution event cameras.IEEE Transactions on Intelligent Transportation Systems, 23(9):15066–15078, 2021

Reference 2

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Observation db795ff2-92e2-4eb6-a684-5e17e0e14061 · outbound

This paper cites A differentiable recurrent surface for asynchronous event-based data.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation A differentiable recurrent surface for asynchronous event-based data

Reference 3

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Observation 1fa79296-1f32-4436-889c-56c750dfa5eb · outbound

This paper cites Explicit motion disen- tangling for efficient optical flow estimation.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Explicit motion disen- tangling for efficient optical flow estimation

Reference 4

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verified fuzzy
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Source-reported events for the cited work

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Observation a7d39459-6ab0-43ce-9e80-0884b4a80e2d · outbound

This paper cites Spatio-temporal recurrent networks for event-based optical flow estimation.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Spatio-temporal recurrent networks for event-based optical flow estimation

Reference 5

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Source-reported events for the cited work

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Observation 596b70be-d037-4bce-95a4-6c2227fc36d0 · outbound

This paper cites Flownet: Learning optical flow with convolutional networks.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Flownet: Learning optical flow with convolutional networks

Reference 6

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Source-reported events for the cited work

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Observation 69767bff-bd4f-4c76-8d64-18f515b10998 · outbound

This paper cites A unifying contrast maximization framework for event cam- eras, with applications to motion, depth, and optical flow estimation.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation A unifying contrast maximization framework for event cam- eras, with applications to motion, depth, and optical flow estimation

Reference 7

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Source-reported events for the cited work

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Observation c5fba685-8e21-48b3-8398-b24d010f149c · outbound

This paper cites Davison, J ¨org Conradt, Kostas Daniilidis, and Davide Scaramuzza.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Davison, J ¨org Conradt, Kostas Daniilidis, and Davide Scaramuzza

Reference 8

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Source-reported events for the cited work

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Observation 579272c7-cad2-4c1a-8db1-8f4eb40112f9 · outbound

This paper cites Im2flow: Motion hallucination from static images for action recogni- tion.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Im2flow: Motion hallucination from static images for action recogni- tion

Reference 9

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Source-reported events for the cited work

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Observation 4577b204-d41d-4bc3-9223-8e41ab01e3ed · outbound

This paper cites End-to-end learning of repre- sentations for asynchronous event-based data.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation End-to-end learning of repre- sentations for asynchronous event-based data

Reference 10

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Source-reported events for the cited work

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Observation 061e737d-28a9-499a-b9b5-294ddf1857ca · outbound

This paper cites Dsec: A stereo event camera dataset for driv- ing scenarios.IEEE Robotics and Automation Letters, 6(3): 4947–4954, 2021.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Dsec: A stereo event camera dataset for driv- ing scenarios.IEEE Robotics and Automation Letters, 6(3): 4947–4954, 2021

Reference 11

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Source-reported events for the cited work

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Observation 3bd6490a-4f90-45b0-a65c-35f14388260c · outbound

This paper cites E-raft: Dense optical flow from event cam- eras.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation E-raft: Dense optical flow from event cam- eras

Reference 12

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Source-reported events for the cited work

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Observation 7b66a9ca-7948-4ea2-8329-34166c95084c · outbound

This paper cites Dense continuous-time optical flow from event cameras.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Dense continuous-time optical flow from event cameras

Reference 13

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation b2639145-4826-4594-a841-a4041165304c · outbound

This paper cites Squeeze-and-excitation net- works.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Squeeze-and-excitation net- works

Reference 14

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 68b10607-e844-4e68-ab86-7eeade4decf9 · outbound

This paper cites Flowformer: A transformer architecture for optical flow.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Flowformer: A transformer architecture for optical flow

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 63b2b1dc-4ab0-41bf-b937-9faa1305c851 · outbound

This paper cites Ccmr: high resolution optical flow estimation via coarse-to-fine context-guided motion reasoning.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Ccmr: high resolution optical flow estimation via coarse-to-fine context-guided motion reasoning

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 6761805f-8ff5-45e1-a686-5c914d946bd0 · outbound

This paper cites Learning to estimate hidden motions with global motion aggregation.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Learning to estimate hidden motions with global motion aggregation

Reference 17

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verified fuzzy
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Source-reported events for the cited work

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Observation 76c92ee3-8715-4eab-a581-dc02a155acf6 · outbound

This paper cites Tea: Temporal excitation and aggregation for action recognition.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Tea: Temporal excitation and aggregation for action recognition

Reference 18

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 06c54873-7ad0-4da2-ac1d-2c0bb9024f20 · outbound

This paper cites Blinkflow: A dataset to push the limits of event-based optical flow estimation.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Blinkflow: A dataset to push the limits of event-based optical flow estimation

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 6a11de4f-e98b-46e8-9e8c-189fa4b30150 · outbound

This paper cites Flow-Guided Sparse Transformer for Video Deblurring.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Flow-Guided Sparse Transformer for Video Deblurring

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:08:11.127769Z digest=sha256:c3254a6a05f8507a0c8a8d1a13c63fc8c9b73f72fea3595977bc75214eb08d72

Observation 9bfcdd1e-1266-4a09-842b-0881eee39a59 · outbound

This paper cites Tma: Temporal motion aggregation for event-based optical flow.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Tma: Temporal motion aggregation for event-based optical flow

Reference 21

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:11.255136Z digest=sha256:18e704411f8f5e6d47e4eac9acfa59c60b36773d6a2122a9bf289c658bdd5e69

Observation 31900f0c-6dc6-4eea-8a0e-89185b714710 · outbound

This paper cites Block-matching optical flow for dynamic vision sensors: Algorithm and fpga implemen- tation.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Block-matching optical flow for dynamic vision sensors: Algorithm and fpga implemen- tation

Reference 22

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:11.400735Z digest=sha256:1a664cae01a8b0cfd9aabeac1108ade93cd583a69234e1acb7d7b43a0a333067

Observation 1e8099b5-7f3f-4b33-af0b-933cf5ef3c42 · outbound

This paper cites ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors

Reference 23

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 286f9d53-46a5-4f92-a71d-40e15f917a95 · outbound

This paper cites Learning by distillation: a self-supervised learning frame- work for optical flow estimation.IEEE transactions on pat- tern analysis and machine intelligence, 44(9):5026–5041,.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Learning by distillation: a self-supervised learning frame- work for optical flow estimation.IEEE transactions on pat- tern analysis and machine intelligence, 44(9):5026–5041,

Reference 24

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 54d22c69-24c1-4966-95e2-45f211417004 · outbound

This paper cites Oiflow: Occlusion-inpainting op- 9 tical flow estimation by unsupervised learning.IEEE Trans- actions on Image Processing, 30:6420–6433, 2021.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Oiflow: Occlusion-inpainting op- 9 tical flow estimation by unsupervised learning.IEEE Trans- actions on Image Processing, 30:6420–6433, 2021

Reference 25

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d5cd0931-f331-4f59-a95e-658eacaa9607 · outbound

This paper cites Decoupled Weight Decay Regularization.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Decoupled Weight Decay Regularization

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:08:11.965497Z digest=sha256:81422e09f6b8cb30c203b0d3f0c05e8c70412057163ab0c77bfc97d1d18c0557

Observation 90387ca5-db25-4704-a29d-6701f0c0e377 · outbound

This paper cites An iterative image reg- istration technique with an application to stereo vision.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation An iterative image reg- istration technique with an application to stereo vision

Reference 27

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:12.066714Z digest=sha256:9452faf54df7d05f350e43c18355ec48b856e0e56c4c7c400983fedce6f556ca

Observation 867ea289-d5dc-4277-9194-b7bac15cbe95 · outbound

This paper cites Single image optical flow estimation with an event camera.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Single image optical flow estimation with an event camera

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-07T11:08:16.986882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 8e34f516-cd4d-4d6c-b5da-0fc7dd0f8ed0 · outbound

This paper cites Taming contrast max- imization for learning sequential, low-latency, event-based optical flow.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Taming contrast max- imization for learning sequential, low-latency, event-based optical flow

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-07T11:08:16.972838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:12.325309Z digest=sha256:10f0b6254ae00cfbb5163673910a70feb129a8289b23fae387ba652dfe53e120

Observation 94ae1992-1143-45fb-a716-728387cbdb9c · outbound

This paper cites Secrets of event-based optical flow.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Secrets of event-based optical flow

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:12.470363Z digest=sha256:5d67f8296cf37a35d95a74194da33015bc1eb439436ac6e17aa4ba9d9a1ddf00

Observation 8c74bc54-3514-44cf-be4a-c9f1864e689b · outbound

This paper cites Super-convergence: Very fast training of neural networks using large learn- ing rates.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Super-convergence: Very fast training of neural networks using large learn- ing rates

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:08:12.617839Z digest=sha256:d2ab791f2092801cb769194b43f1ee10268f91470dd329d7f294e844b9190649

Observation 15558f38-fdb8-49c3-927b-e7b9dfdba7a1 · outbound

This paper cites Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:08:12.730328Z digest=sha256:eb3ca1efd025431cda5ad1fe60c11baff87452c5963e8237f8f68c054f70cfcd

Observation fb8a6dd1-f3a3-4922-9cf0-ae88a55ef797 · outbound

This paper cites Craft: Cross- attentional flow transformer for robust optical flow.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Craft: Cross- attentional flow transformer for robust optical flow

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:16.933844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:12.850676Z digest=sha256:432df2e214d44aa8c34f4324aa85e3ebb987c0760ffc73348b97147f43eddefd

Observation 6544ad6c-5f88-40de-a205-787958a37ad8 · outbound

This paper cites Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:16.919045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:12.992530Z digest=sha256:ebde3d8c3300e712fe0bc3102d553e6d2808dbbbbbf944e6f03f47ec21ccf183

Observation 83ab8da2-0a5c-4bd2-b8ac-db6de5174b01 · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Raft: Recurrent all-pairs field transforms for optical flow

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:16.903299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.036474Z digest=sha256:6ade7b1495d1f436dbea19c8e56dd5be25bff5064fe2b697f6d7f6ce5296856f

Observation 9f574631-51b5-41d1-959b-e72dc0f38e8d · outbound

This paper cites Unsupervised learning of optical flow with cnn-based non-local filtering.IEEE Transactions on Image Processing, 29:8429–8442, 2020.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Unsupervised learning of optical flow with cnn-based non-local filtering.IEEE Transactions on Image Processing, 29:8429–8442, 2020

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:16.757608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.083787Z digest=sha256:ef18d2ad92a9e6996ddea46e9931345291b4c85b109a00799a3c9dd1e7923d7b

Observation 93c19cc4-5853-419b-ad10-8872b4198cf7 · outbound

This paper cites Learning dense and continuous optical flow from an event camera.IEEE Transactions on Image Processing, 31:7237–7251, 2022.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Learning dense and continuous optical flow from an event camera.IEEE Transactions on Image Processing, 31:7237–7251, 2022

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:16.500439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.129694Z digest=sha256:75d1293ee0488ec8a8f771b67295339a46d0a386112a0c11fcac74cce9b3b1be

Observation e2ea3dbe-caad-42f9-a6b1-746b6e4d95aa · outbound

This paper cites Flow dynamics correction for action recognition.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Flow dynamics correction for action recognition

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:16.402427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.172698Z digest=sha256:5b2004b8f385a81bc5d9ae966c833520a31f39c4cb256f6b0d89a0b35677324b

Observation b44e75f1-962b-4d27-a832-335624cfcb9c · outbound

This paper cites Lightweight event-based optical flow estimation via iterative deblurring.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Lightweight event-based optical flow estimation via iterative deblurring

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:16.205374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.219500Z digest=sha256:b6d0b9ca03b9f29f55bf15a16937b28cf08911fe769f8ea5e9d581c0241f983f

Observation db28894d-4328-429d-9997-319532365bca · outbound

This paper cites Unifying flow, stereo and depth estimation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11):13941– 13958, 2023.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Unifying flow, stereo and depth estimation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11):13941– 13958, 2023

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:16.020892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.264153Z digest=sha256:8fc07ac6d0e4055aa86d561672aa837ba73f2aa4fffaf2758c7d87237ebd8c6e

Observation 9493ecfa-a4c9-47e1-93ca-ba07bc545324 · outbound

This paper cites V olumetric correspon- dence networks for optical flow.Advances in neural infor- mation processing systems, 32, 2019.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation V olumetric correspon- dence networks for optical flow.Advances in neural infor- mation processing systems, 32, 2019

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:15.895363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.323062Z digest=sha256:bed03b14c5149da63c785b3da32404fe9bb2973c2012dd5d97d03c24b087b2f3

Observation 8810b768-f6eb-4d05-addf-d24964aafc82 · outbound

This paper cites Folt: Fast multiple object tracking from uav- captured videos based on optical flow.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Folt: Fast multiple object tracking from uav- captured videos based on optical flow

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:15.807147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.458410Z digest=sha256:5ab217ff22d3314caf0a3238e9575597cbbbec4fe11bdf5af467184cb5e28925

Observation e125a785-1ab2-4710-a750-eecabefa0db5 · outbound

This paper cites Spatio- temporal deformable attention network for video deblurring.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Spatio- temporal deformable attention network for video deblurring

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:15.696465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.511914Z digest=sha256:9c5cf2bf13cc209786ae500bdb528a6ff6f9c435064e97db999d4457fca7764c

Observation 13e5a7df-2a4f-47dc-ac6a-3e3d54b0cb61 · outbound

This paper cites Dip: Deep inverse patch- match for high-resolution optical flow.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Dip: Deep inverse patch- match for high-resolution optical flow

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:15.519206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.620664Z digest=sha256:cd49aa368aac935981f3f9b10e2de74bac9cbf789210d8979ae131c308ffa96d

Observation 048566fd-2e75-41bb-9e78-d675504e8b50 · outbound

This paper cites The multi- vehicle stereo event camera dataset: An event camera dataset for 3d perception.IEEE Robotics and Automation Letters, 3 (3):2032–2039, 2018.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation The multi- vehicle stereo event camera dataset: An event camera dataset for 3d perception.IEEE Robotics and Automation Letters, 3 (3):2032–2039, 2018

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:15.400684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.679293Z digest=sha256:4dcb0aa4958f862e3385e3c2cf383510d8a73847d4b27f1db8fdbe6eee639cbd

Observation efd1da0d-c4dc-4483-81f3-599b9c1593bd · outbound

This paper cites EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T11:08:13.780526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:08:13.780526Z digest=sha256:010a4df9ba1b8755ccb175c9759c124dd2348575fdc9963fe509932742a7dadf

Observation b4707bb7-66d1-4bd0-8699-583de9102194 · outbound

This paper cites Unsupervised event-based learning of optical flow, depth, and egomotion.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Unsupervised event-based learning of optical flow, depth, and egomotion

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:08:15.215224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.842902Z digest=sha256:54529a85ac297d11dbedd1e2448381e0b1ef3a6b8b76160f31e11ca1db463a49

Observation 1190b592-4894-4538-8c03-18f1d7b240fe · outbound

This paper cites an unresolved cited work.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:08:15.023402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.887095Z digest=sha256:30b2489a9d5f13309d8efec36643e90afef2f15aae3042bb97e0ae583e936c16

Observation c6258ab3-e6b9-4229-b5ce-213e28a56150 · outbound

This paper cites They primarily perform pixel matching or refinement across multiple spatial reso- lutions in frames.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation They primarily perform pixel matching or refinement across multiple spatial reso- lutions in frames

Reference 49

Resolution
malformed identifier
raw_fallback, observed 2026-08-07T11:08:14.878278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:13.977343Z digest=sha256:5d9d21e39e14b6610b1872092f6942df0078bdff0df315584538be0292ea2725

Observation 634e0889-4293-453b-a194-af4fd52e62fb · outbound

This paper cites 7 presents a qual- itative comparison of our method with other methods on outdoor day1 sequence of the MVSEC [45].

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation 7 presents a qual- itative comparison of our method with other methods on outdoor day1 sequence of the MVSEC [45]

Reference 50

Resolution
malformed identifier
raw_fallback, observed 2026-08-07T11:08:14.719150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:14.051345Z digest=sha256:908ca2680dccbc2a77d9869bd51b1675454411aa4358af6acd926b8d76608081

Observation a0696f9d-4fff-465e-93a2-865a538175f5 · outbound

This paper cites w/o” indicating conv1 removed. We setr= 1to bal- ance accuracy and computations. When compared to “w/o.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation w/o” indicating conv1 removed. We setr= 1to bal- ance accuracy and computations. When compared to “w/o

Reference 51

Resolution
malformed identifier
raw_fallback, observed 2026-08-07T11:08:14.647019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:14.132931Z digest=sha256:2afb78b461a8fefc597cbaa2d81ea5ad51fda1b551b3a2d4609f413b317ee705

Observation ec7e16b7-d999-427b-a2f2-5455a95ecc95 · outbound

This paper cites an unresolved cited work.

EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:08:14.502646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:08:14.190813Z digest=sha256:48f5131d41f8ce1b0c7e5963b04bc77d3d343866835b2c9511a2f35592dc3537

Pith citing papers

No inbound Pith citation observations are available.