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Paper Citation Record · LEDGER

Learning Normal Flow Directly From Event Neighborhoods

As of 12 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 4 inbound Pith citation observations for arXiv:2412.11284.

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

pith.paper-citation-record.v1
2412.11284 v1

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:13:23.293780Z

measured 76 of 76 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:35:26.587738Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T14:14:45.635113Z

Reference resolution

72 of 72 outbound references displayed

  • verified exact7
  • verified fuzzy49
  • unresolved15
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9e9e6f62-941d-41d2-b138-a0aec3f04a73 · outbound

This paper cites https://docs.scipy.org/doc/ scipy / reference / generated / scipy.

Learning Normal Flow Directly From Event Neighborhoods https://docs.scipy.org/doc/ scipy / reference / generated / scipy

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 580315c9-5847-41e0-a7ff-060af917b276 · outbound

This paper cites https : / / elvers.

Learning Normal Flow Directly From Event Neighborhoods https : / / elvers

Reference 2

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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 2026-08-12T06:34:41.77262+00:00.

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Observation b72f2f64-e5bb-4752-a46d-dd996e364083 · outbound

This paper cites Real-time high speed motion prediction using fast aperture- robust event-driven visual flow.

Learning Normal Flow Directly From Event Neighborhoods Real-time high speed motion prediction using fast aperture- robust event-driven visual flow

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.866909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 38858529-ac8a-4443-90a9-7edcc4261828 · outbound

This paper cites Distance surface for event- based optical flow.

Learning Normal Flow Directly From Event Neighborhoods Distance surface for event- based optical flow

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.859957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.078572Z digest=sha256:7161469bb0a11c6a0fee3965b10a18b7306ea2b7339702ff2f93bad5657cbd71

Observation dd3f7c3c-d8b1-4007-9b1a-a88e7c8c1f81 · outbound

This paper cites Contour motion estimation for asynchronous event- driven cameras.

Learning Normal Flow Directly From Event Neighborhoods Contour motion estimation for asynchronous event- driven cameras

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.853235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.081439Z digest=sha256:2f4450dd7a99f874b0a2fa42e3dd4d45a8997fd27f93f1a9c6bcf628188c6214

Observation ef67800a-9d7c-4b58-9605-ca1cd88727a7 · outbound

This paper cites Bio-inspired motion estimation with event-driven sensors.

Learning Normal Flow Directly From Event Neighborhoods Bio-inspired motion estimation with event-driven sensors

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.845668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.084227Z digest=sha256:329d0f7a8246257c40831aaa37ec4133088c6a28282df555ffdfefe16441df11

Observation 3e2d67ff-5525-4c5a-9681-0fbc4987949a · outbound

This paper cites Joint direct estimation of 3d geometry and 3d motion using spatio temporal gradients.

Learning Normal Flow Directly From Event Neighborhoods Joint direct estimation of 3d geometry and 3d motion using spatio temporal gradients

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.838571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 04074efe-8dcb-431e-a825-071921606f3c · outbound

This paper cites Asynchronous frameless event-based optical flow.

Learning Normal Flow Directly From Event Neighborhoods Asynchronous frameless event-based optical flow

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.831177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.091424Z digest=sha256:3beb9e9eaca6bc42f4a1e825492511953af1b3186613349da0589a0ad5c811d6

Observation 0eb8718c-67e1-4d56-9ffd-05c784bdb931 · outbound

This paper cites Event-based visual flow.

Learning Normal Flow Directly From Event Neighborhoods Event-based visual flow

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.823825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.094512Z digest=sha256:96c5573c8dc2ffa9c9d1f38c9f5c8d95d2c43c7a9d9f488db685da510851e09d

Observation 8f90bee5-4230-4278-b150-cd4d73d5118e · outbound

This paper cites Real- time optical flow for vehicular perception with low-and high- resolution event cameras.

Learning Normal Flow Directly From Event Neighborhoods Real- time optical flow for vehicular perception with low-and high- resolution event cameras

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.816927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.097797Z digest=sha256:dd0de1030699fdb4af9d090b0404b98ca9bbe6adf79a84ef318a4f1feb153996

Observation b7d25564-c715-44bf-9ba7-7f0748093648 · outbound

This paper cites Structure from motion: Beyond the epipolar con- straint.

Learning Normal Flow Directly From Event Neighborhoods Structure from motion: Beyond the epipolar con- straint

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.809671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.100759Z digest=sha256:beedb99934cf39e0fd580698aa7d9e0bb52f0a65b51843dac36b217549f683db

Observation 2e8d9379-7e7a-4eb5-8c0d-7efbfbf0ecfd · outbound

This paper cites On event-based optical flow detection.

Learning Normal Flow Directly From Event Neighborhoods On event-based optical flow detection

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.801528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.103690Z digest=sha256:8de50ed11bb2582639f11ec4c6c0baf28266421c8246b1b600980c05bc9a7351

Observation c843aa9b-2c3a-4688-a2ba-8ac028d43329 · outbound

This paper cites EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms.

Learning Normal Flow Directly From Event Neighborhoods EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.106736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.106736Z digest=sha256:f38206f1babf3e512d5f5e1b53ffdffcb64f345340607fa617f6e9e5cf848674

Observation 5e3a8806-d54e-40bc-8f72-b7489860d6e0 · outbound

This paper cites Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation.

Learning Normal Flow Directly From Event Neighborhoods Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation

Reference 14

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:13:23.436924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.110984Z digest=sha256:ccb05e77d36285d7df5c18266177cd09d1485e74217ada041de729c53711668a

Observation 36dbd645-3304-4746-a11d-d2099f986168 · outbound

This paper cites TimeRewind: Rewinding Time with Image-and-Events Video Diffusion.

Learning Normal Flow Directly From Event Neighborhoods TimeRewind: Rewinding Time with Image-and-Events Video Diffusion

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:13:23.426310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.114228Z digest=sha256:cdc27c7cbabec2ad9b65aa098a0b2b072a89b34a4e2434bd9aa1d3af980d7bf2

Observation dbc37052-fbeb-45ec-aafa-0a0ffbf7386b · outbound

This paper cites Optical flow es- timation from event-based cameras and spiking neural net- works.

Learning Normal Flow Directly From Event Neighborhoods Optical flow es- timation from event-based cameras and spiking neural net- works

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.792618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.117561Z digest=sha256:7cfc01a672cc7029c84444feb7d40aa8bfb4516971de37bb48f5af4c09230957

Observation 30c2ee58-5921-44ab-9d73-6ff9e9d67b65 · outbound

This paper cites Passive navigation as a pattern recogni- tion problem.

Learning Normal Flow Directly From Event Neighborhoods Passive navigation as a pattern recogni- tion problem

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.785446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.120403Z digest=sha256:5dad89a58a4e8122b15743b70861ee2049e75c0f584a4629920d92737709f359

Observation a16bffd6-3423-4589-bda7-6667eb4eebe9 · outbound

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

Learning Normal Flow Directly From Event Neighborhoods A unifying contrast maximization framework for event cam- eras, with applications to motion, depth, and optical flow estimation

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.778153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.123345Z digest=sha256:079c8f38eeea6c735dafe259462b42e32a45bcb67e13094d6de2b24c42d020d9

Observation 9509b01f-079d-4b62-a11a-951828f0f93f · outbound

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

Learning Normal Flow Directly From Event Neighborhoods E-raft: Dense optical flow from event cam- eras

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.769882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.126486Z digest=sha256:f51cddfe3f2ae053f222ed55c0bd1e18dfe60da5fa25768aadfe4b1e75b36cf5

Observation f9510d97-50a7-4f15-99df-aed9e26021db · outbound

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

Learning Normal Flow Directly From Event Neighborhoods E-raft: Dense optical flow from event cam- eras

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.762948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.130697Z digest=sha256:d62d56a63a955b886c490938516af520b9dcbeec18571e136ac3e9be8d05ef43

Observation ec341f40-16dc-4d0c-978d-2ce37f5f6ab7 · outbound

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

Learning Normal Flow Directly From Event Neighborhoods Dense continuous-time optical flow from event cameras

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.756037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.133820Z digest=sha256:4b56b441cc616cfb70da1d6a495e6777a6406c68b7da9cf42b25dc31438b8fa7

Observation 47c06a2d-94dc-4296-9459-4985c4addb62 · outbound

This paper cites Self-supervised learning of event-based optical flow with spiking neural networks.

Learning Normal Flow Directly From Event Neighborhoods Self-supervised learning of event-based optical flow with spiking neural networks

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.747113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.136572Z digest=sha256:986186eff4b2cf83965d2676d6ecadc0ce35cea073f93b1432129ebdaca07111

Observation fdc9d177-c58b-4661-8a6b-7a7cf57e42cc · outbound

This paper cites Event-Aided Time-to-Collision Estimation for Autonomous Driving.

Learning Normal Flow Directly From Event Neighborhoods Event-Aided Time-to-Collision Estimation for Autonomous Driving

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:13:23.415678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.139856Z digest=sha256:f46a6c5d42b9d44d326a832ae7a19f0e0963f777537e77174a53e05dbd1efe48

Observation a8d6acde-ea27-4656-8868-1c5f44381316 · outbound

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

Learning Normal Flow Directly From Event Neighborhoods Blinkflow: A dataset to push the limits of event-based optical flow estimation

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.739365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.144017Z digest=sha256:a3c9593da85df363c352eeef55fbc049b6d324a0801b08116d52e763f298e03b

Observation 90c20c9b-a693-40f2-b0fb-5fa766f14b5c · outbound

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

Learning Normal Flow Directly From Event Neighborhoods Tma: Temporal motion aggregation for event-based optical flow

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.731109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.147972Z digest=sha256:895abbcbd29c704b86d98582ff659032b48aca1fc395c57c5625b19b721c0a57

Observation 9563011f-9fa3-4785-9f73-5ab9454c8637 · outbound

This paper cites Adaptive time-slice block- matching optical flow algorithm for dynamic vision sensors.

Learning Normal Flow Directly From Event Neighborhoods Adaptive time-slice block- matching optical flow algorithm for dynamic vision sensors

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.722431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.150918Z digest=sha256:331e344d2c54d1d74418f19ea1e2ff8f1d288dc70b4d07a84d165d21ccd6b6e0

Observation 76073f0a-2126-4c66-978a-b5a8e9dccd26 · outbound

This paper cites Edflow: Event driven opti- cal flow camera with keypoint detection and adaptive block matching.

Learning Normal Flow Directly From Event Neighborhoods Edflow: Event driven opti- cal flow camera with keypoint detection and adaptive block matching

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.714004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.153885Z digest=sha256:90e0e0282a1c5a59a1e44e6dcef0ce73ad10742eeb5a904b9a646dafead62e3f

Observation df318bd9-14b4-4e7d-a26a-765a818791f3 · outbound

This paper cites Event-based Visual Inertial Velometer.

Learning Normal Flow Directly From Event Neighborhoods Event-based Visual Inertial Velometer

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.156723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.156723Z digest=sha256:22b217acb038898a89e856eb98792302ef03064332029358baa4b28de6523560

Observation 7e3a7904-7186-4842-b1fc-d4a5c1439fae · outbound

This paper cites Learning optical flow from event camera with rendered dataset.

Learning Normal Flow Directly From Event Neighborhoods Learning optical flow from event camera with rendered dataset

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.705966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.159980Z digest=sha256:201f63a96c5c021330ee7eb325fe149a45f3782259e53aa8a52109d471697855

Observation 261273da-bae4-4ce2-b888-adb71df54873 · outbound

This paper cites Efficient meshflow and opti- cal flow estimation from event cameras.

Learning Normal Flow Directly From Event Neighborhoods Efficient meshflow and opti- cal flow estimation from event cameras

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.696764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.162972Z digest=sha256:4392b82417ab78e391ec865d45509b81cf9d9f705be01e3460e077c29caf3636

Observation 49f9ce32-eb2b-4873-984d-25381a88dbcb · outbound

This paper cites Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework.

Learning Normal Flow Directly From Event Neighborhoods Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.165906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.165906Z digest=sha256:e9b15cde61dcb282a71592683c173b3afe99a8ed866cab7385ebc8ecfca4540f

Observation d8dce70b-e3ea-43c1-8744-0b2612c0df85 · outbound

This paper cites Lifetime estimation of events from dynamic vision sensors.

Learning Normal Flow Directly From Event Neighborhoods Lifetime estimation of events from dynamic vision sensors

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.688053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.169211Z digest=sha256:54c7f3c423743e549b2c1988a0c583853cd267f9f15ea944805bb4cd7fe38f4b

Observation bc9bdf16-1e21-41d9-9e8a-074af9144e97 · outbound

This paper cites Diffposenet: Di- rect differentiable camera pose estimation.

Learning Normal Flow Directly From Event Neighborhoods Diffposenet: Di- rect differentiable camera pose estimation

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.679398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.173008Z digest=sha256:1477b8a5e5a714c2aa9b73c6e7a1b2a63374e8acd406c8877d931a37e8683934

Observation 3a617136-4574-4e14-8e11-5699e3a23827 · outbound

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

Learning Normal Flow Directly From Event Neighborhoods Taming contrast max- imization for learning sequential, low-latency, event-based optical flow

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.670660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.176086Z digest=sha256:cde7774fc67e6705265aa78199cdb97078be6e370c712661b8ef05eb9141c6e2

Observation faeb8c0f-8aaf-4c86-8f43-18ea0dc4bbc8 · outbound

This paper cites Vertical landing for micro air vehicles using event-based optical flow.

Learning Normal Flow Directly From Event Neighborhoods Vertical landing for micro air vehicles using event-based optical flow

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.662442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.179281Z digest=sha256:e733484fe654c9bda980b76665b6114afecdf45cb06ec6e2f7e38bc4d905ec37

Observation aeae0475-83df-4eab-9c33-5625abc3d503 · outbound

This paper cites Event-based temporally dense optical flow estimation with sequential learning.

Learning Normal Flow Directly From Event Neighborhoods Event-based temporally dense optical flow estimation with sequential learning

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.654214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.182070Z digest=sha256:633a54e07d2bfd3a45e00f849d4994622ca34513cba40872cc65de2a3ecea222

Observation 14a255a8-62e6-416a-a65a-d2703f2a6414 · outbound

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

Learning Normal Flow Directly From Event Neighborhoods Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.184994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.184994Z digest=sha256:0ae752cf106e7481390f6be8f6b0f569e367e83790caffa08c211cbaac395064

Observation 08f01142-702b-4d3e-b339-8393b886d706 · outbound

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

Learning Normal Flow Directly From Event Neighborhoods Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.188130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.188130Z digest=sha256:a4757debdd6c1dc3763e9335d96af98c19babe7e72286a8219bf030901a0137a

Observation d634d419-b0cf-4a6f-9a15-504fed34e6cd · outbound

This paper cites SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition.

Learning Normal Flow Directly From Event Neighborhoods SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.191123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.191123Z digest=sha256:3e0e4b7cc7335fdbce28b6c7cb7a1583100a22d4373d194fe1c43eef47e8d037

Observation 6678e844-ce8d-4ab8-947d-e697d715ad2e · outbound

This paper cites Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba.

Learning Normal Flow Directly From Event Neighborhoods Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.194515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.194515Z digest=sha256:502e1470971128287175881325c7f07349d9e73a9b38ce12780db811080656bc

Observation 98b331b9-0480-4d7d-b593-d76ea8a4eab2 · outbound

This paper cites Motion and Structure from Event-based Normal Flow.

Learning Normal Flow Directly From Event Neighborhoods Motion and Structure from Event-based Normal Flow

Reference 41

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:13:23.381691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.198952Z digest=sha256:317910694aa0843c953117574a60079a748f7986bc0f25a500b8c3374820ef8b

Observation b072d879-10b3-4c26-9b13-4191ce0ceb0e · outbound

This paper cites Eventnet: Asynchronous recursive event processing.

Learning Normal Flow Directly From Event Neighborhoods Eventnet: Asynchronous recursive event processing

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.636858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.202078Z digest=sha256:990bb01d02f2ffa85d9d364077530c0244e8b36e73424b75822e0dc66fda9a23

Observation 56d89dc1-d807-4e75-a2a9-0b79c08d0053 · outbound

This paper cites Fast event-based optical flow estimation by triplet matching.

Learning Normal Flow Directly From Event Neighborhoods Fast event-based optical flow estimation by triplet matching

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.627348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.205455Z digest=sha256:e82bcce702dc0c4952d690ecdaae6ea77c3754cea92feefb144dd08e241eb984

Observation e44256e3-46ab-43bf-b8d1-aa61a8c1beb5 · outbound

This paper cites Secrets of event-based optical flow.

Learning Normal Flow Directly From Event Neighborhoods Secrets of event-based optical flow

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.618758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.208314Z digest=sha256:e8c2658e10dcc0cdd95013ee798c4cd5b7b8c8aa4b731126037212004d37883d

Observation 6f462787-b230-492a-95f4-5c863b4fe36d · outbound

This paper cites Deep Complex Networks.

Learning Normal Flow Directly From Event Neighborhoods Deep Complex Networks

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.211474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.211474Z digest=sha256:8f750f8a55d08806abfa4bc470d6545786d68ee389bbba7ab591b1df2301a2fb

Observation 2af4c8c2-a2f0-4087-93ad-782fe672c3bf · outbound

This paper cites Learning dense and continuous optical flow from an event camera.

Learning Normal Flow Directly From Event Neighborhoods Learning dense and continuous optical flow from an event camera

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.609514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.214368Z digest=sha256:62791ebab998b714ae129a7639044067f00ed7ffeaa1a4a2c5830b3ca70758a1

Observation f8c783c1-6ec2-4079-bca8-4afe30aab232 · outbound

This paper cites Rpeflow: Multimodal fusion of rgb-pointcloud-event for joint optical flow and scene flow estimation.

Learning Normal Flow Directly From Event Neighborhoods Rpeflow: Multimodal fusion of rgb-pointcloud-event for joint optical flow and scene flow estimation

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.601148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.217039Z digest=sha256:b5485316ca685420703968f2ffd08887958db094d7d044f11f2dce6c66f73c52

Observation db56722e-6c55-4379-bfd6-d9c1f5a9e934 · outbound

This paper cites Event-based optical flow via trans- forming into motion-dependent view.

Learning Normal Flow Directly From Event Neighborhoods Event-based optical flow via trans- forming into motion-dependent view

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.592631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.220215Z digest=sha256:3579e1e9e0e2f401c1977e5acb7d1afd140506162441059c87128b8ed0ae1b1b

Observation b6e8ea0b-45be-41d8-a4c8-6cd566dcf861 · outbound

This paper cites Space-time event clouds for gesture recognition: From rgb cameras to event cameras.

Learning Normal Flow Directly From Event Neighborhoods Space-time event clouds for gesture recognition: From rgb cameras to event cameras

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.222742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.222742Z digest=sha256:3d9a3f9aec5122c79b5f4dec7eacb69e9f33f5bf3041e79d6f55ba46439ee376

Observation d55c98d6-954d-43b4-8ace-08b83f91e4cf · outbound

This paper cites Mpct: Multiscale point cloud trans- former with a residual network.

Learning Normal Flow Directly From Event Neighborhoods Mpct: Multiscale point cloud trans- former with a residual network

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.578769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.225421Z digest=sha256:3cf72fe516f91190ba016cfb67a009689961cb868ed1bf48b4bdb46ddf653138

Observation 901473d7-af90-43b5-855a-e81d6d8cac3d · outbound

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

Learning Normal Flow Directly From Event Neighborhoods Lightweight event-based optical flow estimation via iterative deblurring

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.570427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.228027Z digest=sha256:92156c75708bb070c30476210e8006a2637051ac67b02600f0d9cf7eed66e284

Observation 67a5e6b8-f1de-4db0-a47d-b31c1a82a6c3 · outbound

This paper cites Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion.

Learning Normal Flow Directly From Event Neighborhoods Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.230926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.230926Z digest=sha256:88b8a920e59c832e647e24ad32e8be20b3d5563b98e27a89cef96851f82355a5

Observation 30b67ec6-cef4-482a-9b27-b7d22ec34f7d · outbound

This paper cites Event-based Optical Flow on Neuromorphic Processor: ANN vs. SNN Comparison based on Activation Sparsification.

Learning Normal Flow Directly From Event Neighborhoods Event-based Optical Flow on Neuromorphic Processor: ANN vs. SNN Comparison based on Activation Sparsification

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:13:23.359591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.234230Z digest=sha256:3daca243774f451341ed0541a4fd3354c32cfff9a38c046d6bd4efaae9d47e40

Observation f0a9c13b-c327-4cc0-b5be-98cbb9ae3f67 · outbound

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

Learning Normal Flow Directly From Event Neighborhoods Modeling point clouds with self-attention and gumbel subset sampling

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.561905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.237175Z digest=sha256:ae2457ba91e6e89d06ecf78a11ba9dd62522e1a29f7d5ec79ebb93df3ce23916

Observation 4e0ee1cf-03c6-41b9-88e3-eedbac71ec3b · outbound

This paper cites Towards Anytime Optical Flow Estimation with Event Cameras.

Learning Normal Flow Directly From Event Neighborhoods Towards Anytime Optical Flow Estimation with Event Cameras

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.239764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.239764Z digest=sha256:f2b18119675ea7fbae20e18a16e9ff5f336aef2fb5808e5ba651debc439d16a2

Observation 472c2bc1-7095-4031-b171-0a3aa59b4590 · outbound

This paper cites Vector-Symbolic Architecture for Event-Based Optical Flow.

Learning Normal Flow Directly From Event Neighborhoods Vector-Symbolic Architecture for Event-Based Optical Flow

Reference 56

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:13:23.341590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.242612Z digest=sha256:3307068477def2817544dd81521afec15e17d5a2373b000d750d58e03beee772

Observation 1290476e-57c1-4624-a8e5-b9b7cd5db270 · outbound

This paper cites Decodable and Sample Invariant Continuous Object Encoder.

Learning Normal Flow Directly From Event Neighborhoods Decodable and Sample Invariant Continuous Object Encoder

Reference 57

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:13:23.330624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.246074Z digest=sha256:57a1947d81bd7fc6cb4df40bb73eeaa76e1c72b95efa8d563ce7d9ec8df4703f

Observation f7b967b2-1ac5-476d-8543-7f65e74c1300 · outbound

This paper cites A linear time and space lo- cal point cloud geometry encoder via vectorized kernel mix- ture (VecKM).

Learning Normal Flow Directly From Event Neighborhoods A linear time and space lo- cal point cloud geometry encoder via vectorized kernel mix- ture (VecKM)

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.552630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.249171Z digest=sha256:3133b8017579c18c5c2655a5feca69b636eaaa8c44573826c3f3851c355b54b7

Observation 4088b887-d09e-4377-934c-e36ffc214779 · outbound

This paper cites Cross-modal learning for optical flow estimation with events.

Learning Normal Flow Directly From Event Neighborhoods Cross-modal learning for optical flow estimation with events

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.544728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.252215Z digest=sha256:d8c7ca297ac95f25a1cb88c7a9d37c0dea140bde0d40b33af579f690fd64a333

Observation e8ef57db-9989-4ae0-b178-a312c7c26517 · outbound

This paper cites Starting from non-parametric networks for 3d point cloud analysis.

Learning Normal Flow Directly From Event Neighborhoods Starting from non-parametric networks for 3d point cloud analysis

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.536427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.255941Z digest=sha256:9cdf7036f690366d7979d08638ba24518980101045ac76a9fcd3dd4188d2f574

Observation 592d0296-ff2d-483b-8712-545e3648593e · outbound

This paper cites Event-based optical flow estimation with spatio-temporal backpropagation trained spiking neural network.

Learning Normal Flow Directly From Event Neighborhoods Event-based optical flow estimation with spatio-temporal backpropagation trained spiking neural network

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.528425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.258829Z digest=sha256:93b9b1c3d3874f87916dd37f5dbffe8c057b4956c12bec0b0271be6cf9f5b386

Observation e9882cb3-e4e0-4995-bd31-ec543c4554e7 · outbound

This paper cites Point transformer.

Learning Normal Flow Directly From Event Neighborhoods Point transformer

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.261797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.261797Z digest=sha256:543706359604e233d5563a7351ab274e54118b6830bd5fa4a5336faf5e4da668

Observation 55b83fc9-5368-4798-804b-f8ae4039e2b1 · outbound

This paper cites Learning optical flow from continu- ous spike streams.

Learning Normal Flow Directly From Event Neighborhoods Learning optical flow from continu- ous spike streams

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.516514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.264632Z digest=sha256:8db0af1501369d6f1495c061cddf933e8b43296fac3d67fe8c1d731386ab44bd

Observation 6807e95a-c25a-49a8-8502-fb3a3f3c2ef9 · outbound

This paper cites The multi- vehicle stereo event camera dataset: An event camera dataset for 3d perception.

Learning Normal Flow Directly From Event Neighborhoods The multi- vehicle stereo event camera dataset: An event camera dataset for 3d perception

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.507315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.267532Z digest=sha256:905a5da0846874889391278ab9a05e6bc744411cc3a5f157c7d4331bacbbd87e

Observation a0a93122-188e-42de-bf8e-32f57e69967e · outbound

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

Learning Normal Flow Directly From Event Neighborhoods EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-11T15:13:23.270582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:13:23.270582Z digest=sha256:5da26b223f519846a776868b008138c0924a242a4d401c29a62c8e2af2ff28b6

Observation 89d282e2-aa03-4a38-b16b-bad895b93234 · outbound

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

Learning Normal Flow Directly From Event Neighborhoods Unsupervised event-based learning of optical flow, depth, and egomotion

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.499539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.273935Z digest=sha256:20f30eaf073ac8fdb77fd99ef71e0795ead0f508896de21da07e319e598ab45a

Observation 2aa9447f-0667-4edc-aaa5-97eb20a00c5f · outbound

This paper cites These visualiza- tions showcase predictions from models trained on each of the three datasets.

Learning Normal Flow Directly From Event Neighborhoods These visualiza- tions showcase predictions from models trained on each of the three datasets

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.491469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.277356Z digest=sha256:a98069bfa068b38bbcb49def7b4a2f174e8186f62c7adf8c79e645c6129d0f8c

Observation 72efb116-16b8-42b2-a04f-949e8ebc4968 · outbound

This paper cites MVSEC & EVIMO2 both provide frame-based forward optical flows in the distorted camera coordinates.

Learning Normal Flow Directly From Event Neighborhoods MVSEC & EVIMO2 both provide frame-based forward optical flows in the distorted camera coordinates

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.483436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.280313Z digest=sha256:51be6fa1db66fbf21d4e8115accee1739d50386b94e8059e13ec08c31392776c

Observation bd786f83-976e-40f8-8c5f-af4bddaca4d9 · outbound

This paper cites The resulting flows are then scaled such that their unit is in pixels per second.

Learning Normal Flow Directly From Event Neighborhoods The resulting flows are then scaled such that their unit is in pixels per second

Reference 69

Resolution
malformed identifier
raw_fallback, observed 2026-08-11T15:13:23.475828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.283378Z digest=sha256:b2a5a09de39d646a18c4dd7fb24b37d11b2755196fab0bc4a5f2099210024917

Observation f1463c9f-2adb-473e-b0a2-60fd57461032 · outbound

This paper cites The models are trained on EVIMO2-imo training set to better capture the impact of the ablated factors.

Learning Normal Flow Directly From Event Neighborhoods The models are trained on EVIMO2-imo training set to better capture the impact of the ablated factors

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:13:23.466345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.287398Z digest=sha256:ac0f1f95e5d7945042799e573b099aac7aad7ceb5ddbb6c2b90b8edc90888b82

Observation ebe7a9d8-cb9e-47e8-b29a-eea95be346ec · outbound

This paper cites an unresolved cited work.

Learning Normal Flow Directly From Event Neighborhoods Unresolved cited work

Reference 71

Resolution
unresolved
raw_fallback, observed 2026-08-11T15:13:23.458632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:13:23.290658Z digest=sha256:6fe077103fb8134e092d44309085ddc577b05d9bd49828eedca58b5cfe465b3c

Observation 9f027222-d304-46a7-b70d-62acb71ddec6 · outbound

This paper cites an unresolved cited work.

Learning Normal Flow Directly From Event Neighborhoods Unresolved cited work

Reference 72

Resolution
unresolved
raw_fallback, observed 2026-08-11T15:13:23.451044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Pith citing papers

Observation b6b3592e-60e1-41fb-add7-7af37094e3d6 · inbound

Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation cites this paper.

Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation Learning Normal Flow Directly From Event Neighborhoods

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T18:35:26.587738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:35:26.587738Z digest=sha256:1f8e8cacca4965ecdb6b92d8bb6a2190c260676ae2d06ca0841a62d6d143e480

Observation aa4bd4da-d0c3-44fa-9150-34733b689435 · inbound

EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects cites this paper.

EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects Learning Normal Flow Directly From Event Neighborhoods

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T10:52:50.006830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:52:50.006830Z digest=sha256:728417b987f350e8e72350a32896ebab8a2754c26083d368737477ebd1ba7cbe

Observation b7fed3e4-b270-47c9-b82e-9bd5d766a55c · inbound

Motion Segmentation and Egomotion Estimation from Event-Based Normal Flow cites this paper.

Motion Segmentation and Egomotion Estimation from Event-Based Normal Flow Learning Normal Flow Directly From Event Neighborhoods

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-06T15:59:05.659591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:59:05.659591Z digest=sha256:1b6d6c7b6f192bae31124e502d79a2387a0fe8a0bad29696c8df7b9c23c8102f

Observation 27c0f5f8-ec02-43bd-a473-9f1aacbef3b5 · inbound

LC-Flow: Learning Local Continuous Optical Flow and Confidence from events cites this paper.

LC-Flow: Learning Local Continuous Optical Flow and Confidence from events Learning Normal Flow Directly From Event Neighborhoods

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-06-30T14:14:45.636498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-06-30T14:09:45.586362Z digest=sha256:1a04b687543798c4aa1f6acf543bef6b8a2ef4d8190e1fc3a54d6377980d16b2