mlsanityData to 14 Oct 2026

URNet: Uncertainty-aware Refinement Network for Event-based Stereo Depth Estimation

arXiv 2509.18184 ↗First posted 18 Sep 2025cs.CVcited by 4

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Abstract (arXiv)

Event cameras provide high temporal resolution, high dynamic range, and low latency, offering significant advantages over conventional frame-based cameras. In this work, we introduce an uncertainty-aware refinement network called URNet for event-based stereo depth estimation. Our approach features a local-global refinement module that effectively captures fine-grained local details and long-range global context. Additionally, we introduce a Kullback-Leibler (KL) divergence-based uncertainty modeling method to enhance prediction reliability. Extensive experiments on the DSEC dataset demonstrate that URNet consistently outperforms state-of-the-art (SOTA) methods in both qualitative and quantitative evaluations.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 31 of its 49 references that are in the map. First posted on arXiv 18 Sep 2025.

Builds on

31 of 49 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  5. 2015Fast R-CNNcited by 2,933
  6. 2017Deformable Convolutional Networkscited by 1,488
  7. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,059
  8. 2019Event-based Vision: A Surveycited by 1,016
  9. 2018Pyramid Stereo Matching Networkcited by 489
  10. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 360
  11. 2021DSEC: A Stereo Event Camera Dataset for Driving Scenarioscited by 303
  12. 2019Group-wise Correlation Stereo Networkcited by 235
19 more
  1. 2022CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformerscited by 231
  2. 2020AANet: Adaptive Aggregation Network for Efficient Stereo Matchingcited by 202
  3. 2018Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Carscited by 200
  4. 2022Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlationcited by 197
  5. 2023Iterative Geometry Encoding Volume for Stereo Matchingcited by 190
  6. 2020Hierarchical Neural Architecture Search for Deep Stereo Matchingcited by 166
  7. 2022CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flowcited by 166
  8. 2018Bounding Box Regression with Uncertainty for Accurate Object Detectioncited by 143
  9. 2024Demystify Mamba in Vision: A Linear Attention Perspectivecited by 102
  10. 2020A Survey on Deep Learning Techniques for Stereo-based Depth Estimationcited by 90
  11. 2018Continuous-time Intensity Estimation Using Event Camerascited by 68
  12. 2018Semi-Dense 3D Reconstruction with a Stereo Event Cameracited by 67
  13. 2024Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Failcited by 47
  14. 2018Realtime Time Synchronized Event-based Stereocited by 28
  15. 2024Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detectioncited by 28
  16. 2024Playing to Vision Foundation Model's Strengths in Stereo Matchingcited by 23
  17. 2024Temporal Event Stereo via Joint Learning with Stereoscopic Flowcited by 12
  18. 2019Neuromorphic Visual Odometry System for Intelligent Vehicle Application with Bio-inspired Vision Sensorcited by 11
  19. 2024LiDAR-Event Stereo Fusion with Hallucinationscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.