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Paper Citation Record · LEDGER

Learning Normal Flow Directly From Event Neighborhoods

As of 22 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 5 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 77 of 77 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:01:55.938656Z

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

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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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.078572Z digest=sha256:d930599ed26acb5b98f6bc18b2d49cb8a6f1c9ce1a311edcf6db9487660afa0f

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

Resolution
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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.081439Z digest=sha256:193c4c287b0c8f7cf7dbf0ec0b96b886e37c41a3de6aeabf7cb2573b96aaa3b3

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.084227Z digest=sha256:7074db7d5540664567b292a3c7e5365693d4ce5204cd1638c66a344d3a0d50e2

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

Resolution
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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.087406Z digest=sha256:fc354ffb65677147a5ffe355c7a0f65ad82d5d83be4fdd8699540de5c31e2e1f

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.091424Z digest=sha256:d682cae43021a09b03e26056c04c73817ee05c73aedc545534c48683b111ef48

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.094512Z digest=sha256:35b417eea272fd40165d38bc8996eebc89e57cbd71411d1fcc2dac1ccf1ca2bd

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.097797Z digest=sha256:fddc5c8456807707508f145c25c5db6f985cd427c56c2ac691c9149951d95204

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.100759Z digest=sha256:36f724c119a2b6e66f2250379ef73d9b7b3f010e8b1b7468732abdd30852623d

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.103690Z digest=sha256:55299696f6adf9a00f9967eb74d1bea2578c8743e7f674905edef07cb3605494

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=sha256:46e33bc4a783bd2ff776b6a5a2b37f9b8897a87cd171cf69cb10407d7f30062f

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.110984Z digest=sha256:d03365e15ee8192e9cec8940b8b9cac506dbd3158cc8d9f290fe285a57164b2b

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.114228Z digest=sha256:9c90fed18e0c9fe8d86f9d5d833137f7c1c88e14844dd9ed07337c89d094384c

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.117561Z digest=sha256:4523332e9d1f66cdb6027d12d60078fa99b6583f34e947b9d39c9aaf1c94d6e7

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.120403Z digest=sha256:508543d20c828484bde108e57e65fa94e3c11e35f3eac67ed3276c516c39329f

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.123345Z digest=sha256:97c1474e3f23d9e01b74e3b63f1be3b8ff45af89d2b820689238c07a4b84fba7

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.126486Z digest=sha256:33a9b2108a0b0c1b055c1273d5cef660b84fe5e5f6c3eddff5229d7b03928416

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.130697Z digest=sha256:471623e9e1cc2ea5c9b0f1576c4808fe1f861bd4b612020c00cffc124dfad57c

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.133820Z digest=sha256:72d5ec05d06f545b1b6d6135ba2a5d8bdb12f25d0f95ac95349d8b5943ae3de8

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.136572Z digest=sha256:f4cce39703fed9ad57b3bf74130a42d462b3cf27467b0da721258733a6cd374a

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.139856Z digest=sha256:fb715c447ff7a4ecca7b0fd0c8c06254a10d08f7ddddf7784fc678bf90ab07e8

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.144017Z digest=sha256:4303c3ac2f5acfc124e9e4b9294714def31e417278d86984070f355eb1811f25

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.147972Z digest=sha256:eabb035ac9a3c1a9b1a5cbefa0057b0d02e0e060118b3da610d2e0c567d536e0

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.150918Z digest=sha256:982047f6fa3465b65941494763c0e18d56fc181d85a80f6670ea2daf5599e57d

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.153885Z digest=sha256:58e53d0fc9de286ea8c0ea89a85cdf09f94b0a6cd0d304bade0ca50c314f7443

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:a3508b1a151c8d24a34c19ce079e9a2801d317cdfc0ebdb6c1edc49f1aad9c1c

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.159980Z digest=sha256:6bdd6104033657902b5a148e4f680d9959614d3fb856cb046d6c6411bb6dd557

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.162972Z digest=sha256:c544b8086c418a45f85746f4860d61b34e2839f5dc0b4f5630e8b826175ade20

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=sha256:6b52858e37c733c44239dfd40a846b18969e8a8b06f49d84dc866aed065da2fc

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.169211Z digest=sha256:c21e60084d4116746485352e56074b5220ae438f0d198dc269fe9976d4880346

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.173008Z digest=sha256:bc27e1c260268505e50ccdda5e8578a8c36341321ed9d25ea05618bae123d102

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.176086Z digest=sha256:10deb71bbcc13515449f02c2936ce98f5e900aa6c9066cd12c753fc254677dd6

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

Resolution
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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.179281Z digest=sha256:f21a806a5e5abe59e448065fb375d921cbc74d6c8eb1084104eb60862db7ef0f

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.182070Z digest=sha256:c5817496412b2842676a4029c6b38dc5ee1130e82b4b179ed96d3bd1c00c0697

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:aa53bf069ea22cf145a63918c7859e78a64b1fd19070e5c6e24f47da35ec6327

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=sha256:8f2ea9aa59168491928edc341d4bb62f867fc01ff6fe1ed85f68f2b1f4b60df5

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:f690efb236b19aa42fd797de02f9b43495064b9544b2d5e69e666b42e9330a53

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=sha256:0c69f7b1c6cae7387678915b8c910a7242b8b86156002a868b9296711315fec9

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.198952Z digest=sha256:2dab8228636685c5d4d6befd89f6a847d1d7ce85669694dd1e903eed16d4a37c

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.202078Z digest=sha256:bab9b709452b01174e51d4a3d2007ce313c6faec91368ab2b708c9a67be17055

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.205455Z digest=sha256:ebc3f430dba4b5aaa228339f97ad407b68cacb1ef23eae9d48a7dd5b7c49fd95

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.208314Z digest=sha256:10f451842695378643f32c91dee813427c075088448d16956ab7ded50f0520f9

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=sha256:08ed73acc6fc58db66f8ec891095b2bce7f94fb4e28439aae7fc0e59bfc6b163

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.214368Z digest=sha256:caa15729a778fe2fa0ee863b03cd16d6f9d1630596b2db4ccf280500f005a84b

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.217039Z digest=sha256:6ab980ce1eb91ab31e10291317074c9213256c84a3791dfc778069ea13ce5659

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.220215Z digest=sha256:46f9d93cd0518ad8bd912f12d8a860eca5d2d32711c39b9e08fc3d1d889ae427

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=sha256:6a1d70cfaefc63e0b4a2dd5538fc0e12bf59d504cf592a60b7eda097c9c88338

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.225421Z digest=sha256:0ece7db16f267b04c71b0b0b7d3dff9aa0949da7d0ec1869b87a137ecdf233f3

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.228027Z digest=sha256:99713c3a22069a39cd23a7fba4b113e6f1e69a28fd3522d948ca0d6079213bd3

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=sha256:730d5b8e69c9adf29e59849935e155fe0c669b56793ec0642b53975b364169fd

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.234230Z digest=sha256:2d8b648f540140aeae2738862e13c8ac253d04e1b8b2d8401df43ac789d939bb

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.237175Z digest=sha256:7f8a44adc0af716777a2d7698d40b172867ca3a596aefb706cc500b76e2e62f6

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=sha256:95864f4b0d178e05af8695ef67c92880f5ca29543b0af4880c7e78223b5c6a70

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.242612Z digest=sha256:48f3c3b9e1a458d811529ef338d0fb5692c217bcb731cb0bbb4dbbd3835969de

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.246074Z digest=sha256:cfb301706be8250a5efb8947718609728c41533ec9b4e8ea34d3e0627510db42

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.249171Z digest=sha256:64759d508c3fcc868ccf0ed7627b68d7fe6e4b350998a6adaa9b44839605759a

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.252215Z digest=sha256:be863fbe00d46d04478efced4692632e47742d0dec1cc1ce372aa60eae5779ef

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.255941Z digest=sha256:b074c03d46c73c5916e694840d968bc639459eec00a3b5d829710accfec2a9d1

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.258829Z digest=sha256:ecf9bb6bb70d73b8092869ab9bb9c64efa8ac7634de8c7cda80913f022bffbab

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=sha256:7459cc6ab8245f5f27cb287ea11ce9d9d107b5bd56c7f17a8941cde270496644

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.264632Z digest=sha256:916fe9d6f672cf241b8ce69b8fb00b959937dc75ecc5e7d3d0b289d9901d16b1

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.267532Z digest=sha256:7936b12dfac4c1be32c095c4873bee15679d43bf59ae75fc2388113f3b441966

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:f3ebff5ffbe8521c6744c6fd39bec25c88133c9cc76247c785b5447a5ced07bb

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.273935Z digest=sha256:5bea006b237ecee695f32ae6c080cd5790b2935c5d44f71a3913735cadf62903

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.277356Z digest=sha256:81739fe86681f9945faad0e8f345ceb4073831179ff7a99e8f0a2acb0fe9e604

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.280313Z digest=sha256:11cdbd75c770c821b4abbdbdfc3842a5fdce4a5960d9468b14eb67ad6f4c96e8

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.283378Z digest=sha256:c6b6bc4a45a145b17ee0ca6cd4024a57e274e618f0dc8bf820f8b0b849ba5756

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.287398Z digest=sha256:f244a788d5e9a53848bb7e1b5614cabd2a59393810f1cad445daed8f151234ef

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.290658Z digest=sha256:0d8c772d0b798f6e695fe59bb0443575dc99515d5bb9752da2ff4b09d9dd8937

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T15:13:23.293780Z digest=sha256:1b89904cb9783f286637456f0d993c066dcbe7aabc516755e4992f33cd6ec36d

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=sha256:929568fa8336a8fce8e5172989912c0a3bcfffd81f6a0c8b93b88a14c4965ff0

Observation 2e8ecc71-ca48-4642-a65a-764b21bb5a04 · inbound

A Real-Time Event-Based Normal Flow Estimator cites this paper.

A Real-Time Event-Based Normal Flow Estimator Learning Normal Flow Directly From Event Neighborhoods

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-16T06:01:55.938656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T06:01:55.938656Z digest=sha256:a04833e344629c7fc4f2cd9ec954a557d5758228c34e3bc48fed9c7fa206c0ca

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=sha256:7526b39c500deb14baacb35e1168b3dc58e95a5d04b3da6249905e57a232e3f7

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:f390e35393e9b82291070fdfcba6d65c0e8ae1e41f069ac8a486625aba6e9d55

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 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T14:09:45.586362Z digest=sha256:d595bf25b095dd02bbf5b8abc2b4303a385c38926d9a308c543409fd13d5a252