DELTA: Dense Depth from Events and LiDAR using Transformer's Attention | Paper Citation Record

Observation fca40964-ec1a-453d-b8d7-64a9214f4d96 · outbound

This paper cites an unresolved cited work.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:52:43.922873Z

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.

Observation 415a1d45-ebb0-4660-a64c-61cdad3a6aca · outbound

This paper cites A 240×180 130 dB 3 µs latency global shutter spatiotemporal vision sensor.IEEE Journal of Solid-State Circuits, 49:2333–2341, 2014.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention A 240×180 130 dB 3 µs latency global shutter spatiotemporal vision sensor.IEEE Journal of Solid-State Circuits, 49:2333–2341, 2014

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.908651Z

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.

Observation 6a85669a-4781-4398-a068-2de59f6f0679 · outbound

This paper cites Learning to estimate two dense depths from LiDAR and event data.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Learning to estimate two dense depths from LiDAR and event data

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.894476Z

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.

Observation a0af04bb-ca98-4e5c-8dac-018540b449d9 · outbound

This paper cites End-to- end object detection with transformers.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention End-to- end object detection with transformers

Reference 4

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Observation 8ea3dfba-a98d-41d0-a20d-f2636414acec · outbound

This paper cites an unresolved cited work.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:52:43.871332Z

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.

Observation c76b8546-1015-4796-8869-748b584d766d · outbound

This paper cites Low-latency monocular depth estimation us- ing event timing on neuromorphic hardware.CVPRW, pages 4071–4080, 2023.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Low-latency monocular depth estimation us- ing event timing on neuromorphic hardware.CVPRW, pages 4071–4080, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.858570Z

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.

Observation 824cf915-a1ff-45be-b509-b5c41907ea8d · outbound

This paper cites Learning adaptive dense event stereo from the image domain.CVPR, pages 17797–17807, 2023.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Learning adaptive dense event stereo from the image domain.CVPR, pages 17797–17807, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.845544Z

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.

Observation a41a2443-f57f-4bff-a69c-a4e578779e02 · outbound

This paper cites Learning phrase representations using RNN encoder-decoder for statistical machine translation.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Learning phrase representations using RNN encoder-decoder for statistical machine translation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.831213Z

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.

Observation 86cfba6d-dc67-409f-97ad-1cb01d3ebedd · outbound

This paper cites MULi-Ev: Maintaining unperturbed LiDAR-event calibra- tion.CVPRW, pages 4579–4586, 2024.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention MULi-Ev: Maintaining unperturbed LiDAR-event calibra- tion.CVPRW, pages 4579–4586, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.816978Z

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.

Observation bbc5fe6f-2395-4a56-bcec-5c54d9c1c17b · outbound

This paper cites Dense depth-map estimation based on fusion of event camera and sparse LiDAR.IEEE Transactions on Instrumentation and Measurement, 71:1– 11, 2022.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Dense depth-map estimation based on fusion of event camera and sparse LiDAR.IEEE Transactions on Instrumentation and Measurement, 71:1– 11, 2022

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.802625Z

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.

Observation 227a17df-0ff6-41c3-8d29-8c807bafa507 · outbound

This paper cites CARLA: An open urban driving simulator.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention CARLA: An open urban driving simulator

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.788686Z

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.

Observation 347ea9a4-db68-405d-90f7-4e8bf8f58d3e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.774348Z

Source-reported events for the cited work

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Observation 78db5bb0-1977-43be-beb1-7357e89dcb10 · outbound

This paper cites Depth map prediction from a single image using a multi-scale deep net- work.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Depth map prediction from a single image using a multi-scale deep net- work

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.760491Z

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.

Observation b4a83fb7-137f-4006-af74-8559ff25244e · outbound

This paper cites an unresolved cited work.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:52:43.747306Z

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.

Observation 24c237b7-3088-458d-a177-6b851fed5621 · outbound

This paper cites Derpa- nis, and Davide Scaramuzza.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Derpa- nis, and Davide Scaramuzza

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.732882Z

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.

Observation f1c8f38d-ca10-422f-b9cd-6fbcd1945fb6 · outbound

This paper cites Combining events and frames using recurrent asynchronous multimodal net- works for monocular depth prediction.IEEE Robotics and Automation Letters, 6:2822–2829, 2021.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Combining events and frames using recurrent asynchronous multimodal net- works for monocular depth prediction.IEEE Robotics and Automation Letters, 6:2822–2829, 2021

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.719259Z

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.

Observation 593835f6-d130-419c-9ef1-88cbb59e4723 · outbound

This paper cites Recurrent vision transformers for object detection with event cameras.CVPR, pages 13884–13893, 2023.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Recurrent vision transformers for object detection with event cameras.CVPR, pages 13884–13893, 2023

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.704501Z

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.

Observation 6419fb0f-ef38-4e05-ba19-a1bcb310366a · outbound

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

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention DSEC: A stereo event camera dataset for driv- ing scenarios.IEEE Robotics and Automation Letters, 6: 4947–4954, 2021

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.690247Z

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.

Observation 3cb4b357-0f36-4e04-b131-561c1f185ca4 · outbound

This paper cites Multi-event-camera depth estimation and outlier rejection by refocused events fusion.Advanced Intelligent Systems, 4, 2022.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Multi-event-camera depth estimation and outlier rejection by refocused events fusion.Advanced Intelligent Systems, 4, 2022

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.675893Z

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.

Observation aceb9d49-c327-4ffa-88fb-ec00f2c4e631 · outbound

This paper cites Hierarchical neural memory network for low latency event processing.CVPR, pages 22867–22876, 2023.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Hierarchical neural memory network for low latency event processing.CVPR, pages 22867–22876, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.661473Z

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.

Observation c4f0d98d-8724-4ce8-a351-db880b6fd60d · outbound

This paper cites Learning monocular dense depth from events.3DV, pages 534–542, 2020.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Learning monocular dense depth from events.3DV, pages 534–542, 2020

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.646944Z

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.

Observation 0ed5379e-ba34-423b-a85c-142832c79108 · outbound

This paper cites LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:52:43.206431Z

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.

Observation b41c19ed-ac4d-4774-b131-3fe18f39bdd5 · outbound

This paper cites Mukhopadhyay.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Mukhopadhyay

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.632676Z

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.

Observation 127d6051-ca15-4978-9e42-030c88eda05d · outbound

This paper cites an unresolved cited work.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:52:43.619089Z

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.

Observation b543bbf3-b6b9-4596-a17e-54dd5dbd7f5a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Adam: A Method for Stochastic Optimization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T00:52:42.906908Z

Source-reported events for the cited work

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Observation ad284e17-6b9b-48c8-a01e-d96843928da8 · outbound

This paper cites Enhancing 3-D LiDAR point clouds with event-based camera.IEEE Transactions on Instrumentation and Measurement, 70:1–12, 2021.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Enhancing 3-D LiDAR point clouds with event-based camera.IEEE Transactions on Instrumentation and Measurement, 70:1–12, 2021

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.606433Z

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.

Observation b614c43f-fa75-4714-b973-1a1ff78301c3 · outbound

This paper cites Event Transformer.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Event Transformer

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T00:52:42.914653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fb0b2eb-3c91-44df-b45b-488695e9b753 · outbound

This paper cites PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:52:43.154636Z

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.

Observation 7e4897d1-77f8-4cc0-9466-96e7c44231d6 · outbound

This paper cites Event-based Monocular Dense Depth Estimation with Recurrent Transformers.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Event-based Monocular Dense Depth Estimation with Recurrent Transformers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T00:52:42.923522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccec1b89-7970-45b2-8259-9ee28d916584 · outbound

This paper cites Stereo depth from events cameras: Concentrate and focus on the future.CVPR, pages 6104–6113, 2022.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Stereo depth from events cameras: Concentrate and focus on the future.CVPR, pages 6104–6113, 2022

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.592290Z

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.

Observation 11c91edc-9d44-4620-9a50-db3535862c65 · outbound

This paper cites Time-to-contact map by joint estimation of up-to-scale inverse depth and global motion using a single event camera.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Time-to-contact map by joint estimation of up-to-scale inverse depth and global motion using a single event camera

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.578387Z

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.

Observation 0305cdd4-d184-4cdf-a3aa-1ec2573af990 · outbound

This paper cites GET: Group event transformer for event-based vision.ICCV, 2023.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention GET: Group event transformer for event-based vision.ICCV, 2023

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.563729Z

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.

Observation dfad6d91-39ac-4e76-bd87-d7c4e1fc9f26 · outbound

This paper cites Cot- tereau, and Timoth´ee Masquelier.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Cot- tereau, and Timoth´ee Masquelier

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.548774Z

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.

Observation 5acf94b1-0c6a-4b73-8878-d3a70e745b54 · outbound

This paper cites U- Net: Convolutional networks for biomedical image segmen- tation.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention U- Net: Convolutional networks for biomedical image segmen- tation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.533647Z

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.

Observation efd10226-9449-4992-8e57-3b82d017e693 · outbound

This paper cites Event transformer.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Event transformer

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.518606Z

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.

Observation 6e9b3eb5-29f2-4748-b4ab-a57abc9f5c46 · outbound

This paper cites Event Transformer+. A multi-purpose solution for efficient event data processing.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Event Transformer+. A multi-purpose solution for efficient event data processing

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:52:43.116549Z

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.

Observation fb838446-33b0-4806-8cc7-2a35e5044035 · outbound

This paper cites Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:52:43.095525Z

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.

Observation 3ab23e8c-0a03-4870-b392-1222efe8bc6b · outbound

This paper cites Dynamic stereo vision system for real-time tracking.Proceedings of 2010 IEEE International Sympo- sium on Circuits and Systems, pages 1409–1412, 2010.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Dynamic stereo vision system for real-time tracking.Proceedings of 2010 IEEE International Sympo- sium on Circuits and Systems, pages 1409–1412, 2010

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.503800Z

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.

Observation cb30fbba-9319-48d6-9916-5e6071d5d41d · outbound

This paper cites An event-driven stereo system for real-time 3-D 360◦ panoramic vision.IEEE Transactions on Industrial Electronics, 63:418–428, 2016.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention An event-driven stereo system for real-time 3-D 360◦ panoramic vision.IEEE Transactions on Industrial Electronics, 63:418–428, 2016

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.489774Z

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.

Observation e03b01c5-29d8-467a-b820-9627bf1c58d1 · outbound

This paper cites Calibration of event-based camera and 3D Li- DAR.2018 WRC Symposium on Advanced Robotics and Automation (WRC SARA), pages 289–295, 2018.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Calibration of event-based camera and 3D Li- DAR.2018 WRC Symposium on Advanced Robotics and Automation (WRC SARA), pages 289–295, 2018

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.476671Z

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.

Observation f64147fa-93fb-48d6-b2a7-98ccf39aff70 · outbound

This paper cites L2E: Lasers to events for 6- DoF extrinsic calibration of lidars and event cameras.ICRA, pages 11425–11431, 2023.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention L2E: Lasers to events for 6- DoF extrinsic calibration of lidars and event cameras.ICRA, pages 11425–11431, 2023

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.463848Z

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.

Observation 04e2f847-5d6c-42e2-9b03-62142ee1c9b5 · outbound

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

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention RAFT: Recurrent all-pairs field transforms for optical flow

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.449741Z

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.

Observation bbe1fa3f-cebf-4d23-bbb1-73465c26b6b0 · outbound

This paper cites Andrade-Cetto.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Andrade-Cetto

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.435201Z

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.

Observation 5dc8850a-ae42-4d1a-bf95-42f8e93587a8 · outbound

This paper cites DeMoN: Depth and motion network for learning monocular stereo.CVPR, pages 5622–5631, 2016.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention DeMoN: Depth and motion network for learning monocular stereo.CVPR, pages 5622–5631, 2016

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.420668Z

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.

Observation 849b3745-5b8e-41d5-87de-058bd3acf9da · outbound

This paper cites Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.406165Z

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.

Observation 3634f204-ec92-4e42-ac6c-64c9a38de54e · outbound

This paper cites Exploiting spatial sparsity for event cameras with visual transformers.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Exploiting spatial sparsity for event cameras with visual transformers

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.391255Z

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.

Observation 349d29d8-bdae-41ff-944a-de2df6f7e8bd · outbound

This paper cites Adrian, Daniel Cremers, and J ¨org Conradt.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Adrian, Daniel Cremers, and J ¨org Conradt

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.375913Z

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.

Observation c806f187-a0d8-415e-a71a-e2a71939cf49 · outbound

This paper cites Event- based video reconstruction using transformer.ICCV, pages 2543–2552, 2021.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Event- based video reconstruction using transformer.ICCV, pages 2543–2552, 2021

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.361700Z

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.

Observation c7575526-a9ff-4963-9e2b-cae10c476d2e · outbound

This paper cites Kumar, and Kostas Daniilidis.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Kumar, and Kostas Daniilidis

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.347760Z

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.

Observation e86fb34a-2eec-4a2b-a9e5-1bb3b2b43678 · outbound

This paper cites Unsupervised event-based learning of op- tical flow, depth, and egomotion.CVPR, pages 989–997,.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Unsupervised event-based learning of op- tical flow, depth, and egomotion.CVPR, pages 989–997,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.333597Z

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.

Observation 4cfe5634-cb65-4103-ae22-5116199cc2b2 · outbound

This paper cites From chaos comes order: Ordering event repre- sentations for object recognition and detection.ICCV, 2023.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention From chaos comes order: Ordering event repre- sentations for object recognition and detection.ICCV, 2023

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.319408Z

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.

Observation 2d0cbe19-1f8d-4ab6-b95c-36d9a7f8b4a8 · outbound

This paper cites 3 is given in Fig.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention 3 is given in Fig

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.306041Z

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.

Observation 71475f8d-df83-49f9-997c-5f5d9abd782a · outbound

This paper cites 10 to 15.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention 10 to 15

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.292919Z

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.

Observation be774ab4-7109-4195-b03f-6a3c5121eda0 · outbound

This paper cites an unresolved cited work.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:52:43.278043Z

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.

Observation 52e6ee57-83e7-4e57-b874-10f5d2ab8844 · outbound

This paper cites No Encoding Head.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention No Encoding Head

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.264490Z

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.

Observation 7d9896c0-f4a1-4dce-b221-a2c46c0e4ee9 · outbound

This paper cites 17 to 20.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention 17 to 20

Reference 56

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T00:52:43.250386Z

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.

Observation c3028423-bfb8-41bc-8be9-c7e320b22a45 · outbound

This paper cites 21, showing the quality of the results for both day and night scenes despite the sparse and low-resolution input event and LiDAR data.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention 21, showing the quality of the results for both day and night scenes despite the sparse and low-resolution input event and LiDAR data

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:52:43.235586Z

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.

Observation 580a5379-8733-4f13-8198-1b81de633877 · outbound

This paper cites an unresolved cited work.

DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:52:43.221110Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers