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NeuroLiDAR: Adaptive Frame Rate Depth Sensing via Neuromorphic Event-LiDAR Fusion

arXiv 2605.16805 ↗First posted 16 May 2026cs.CVcited by 0

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Abstract (arXiv)

LiDARs are widely used for 3D depth reconstruction, but their performance is often limited by inherent hardware constraints that impose trade-offs between range, spatial resolution, and frame rate. Many LiDAR systems typically operate at low frame rates (e.g., 5-10 Hz), prioritizing long-range sensing over responsiveness to rapid scene changes. We present NeuroLiDAR, an adaptive depth sensing framework that achieves effective frame rates of up to ≈66 Hz by fusing temporally sparse LiDAR data with temporally dense inputs from neuromorphic event cameras. NeuroLiDAR integrates two components: event-based keyframe detection and event-guided depth extrapolation, to dynamically adjust the sensing rate in response to scene dynamics. To evaluate our approach, we introduce ELiDAR, a dataset spanning outdoor and indoor scenarios, and show that NeuroLiDAR reduces depth reconstruction error by ≈29% in RMSE while achieving adaptive frame rates between 27.8-47.3 Hz. Our code and dataset are available at https://github.com/darshanakgr/neurolidar.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 24 references that are in the map. First posted on arXiv 16 May 2026.

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12 of 24 references.

References · 12

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  3. 2019Event-based Vision: A Surveycited by 1,016
  4. 2018The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perceptioncited by 254
  5. 2021Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Predictioncited by 90
  6. 2020Learning Monocular Dense Depth from Eventscited by 85
  7. 2023Optical flow estimation from event-based cameras and spiking neural networkscited by 33
  8. 2023Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolutioncited by 33
  9. 2023EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditionscited by 19
  10. 2023EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mappingcited by 9
  11. 2024METER: a mobile vision transformer architecture for monocular depth estimationcited by 7
  12. 2023Learning to Estimate Two Dense Depths from LiDAR and Event Datacited by 5

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