Pith Number

pith:ESC4L3TX

pith:2026:ESC4L3TXHIFYM7JAD3K4K7DRMR

not attested not anchored not stored refs resolved

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

Archan Misra, Darshana Rathnayake, Dulanga Weerakoon, Meera Radhakrishnan

NeuroLiDAR fuses event camera streams with sparse LiDAR scans to raise effective depth frame rates to around 66 Hz while cutting reconstruction error by 29 percent.

arxiv:2605.16805 v1 · 2026-05-16 · cs.CV

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\pithnumber{ESC4L3TXHIFYM7JAD3K4K7DRMR}

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1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

NeuroLiDAR achieves effective frame rates of up to ≈66 Hz by fusing temporally sparse LiDAR data with temporally dense inputs from neuromorphic event cameras, reducing depth reconstruction error by ≈29% in RMSE while achieving adaptive frame rates between 27.8-47.3 Hz.

C2weakest assumption

Event-based keyframe detection and event-guided depth extrapolation can reliably adapt the LiDAR sensing rate across varied indoor and outdoor scenes without introducing large extrapolation errors or missing critical motion.

C3one line summary

NeuroLiDAR adaptively boosts LiDAR frame rates to 27.8-66 Hz via event-camera fusion and cuts depth RMSE by 29% on a new ELiDAR dataset.

References

26 extracted · 26 resolved · 0 Pith anchors

[1] Event-based vision: A survey, 2022

[2] Event-Based Frame Interpolation with Ad-hoc Deblurring 2023

[3] Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction, 2021

[4] Dense depth- map estimation based on fusion of event camera and sparse lidar, 2022

[5] Learning spatial- temporal implicit neural representations for event-guided video super- resolution, 2023

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:23.149276Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2485c5ee773a0b867d201ed5c57c71646e239f2c13b2fbfecddbe70291dc1a13

Aliases

arxiv: 2605.16805 · arxiv_version: 2605.16805v1 · doi: 10.48550/arxiv.2605.16805 · pith_short_12: ESC4L3TXHIFY · pith_short_16: ESC4L3TXHIFYM7JA · pith_short_8: ESC4L3TX

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ESC4L3TXHIFYM7JAD3K4K7DRMR \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2485c5ee773a0b867d201ed5c57c71646e239f2c13b2fbfecddbe70291dc1a13

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "9f7108a7464208273451b8eb4a38acdcf903bfe7de9969e3ba2b28a24fedf369",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-16T04:21:59Z",
    "title_canon_sha256": "73d142f88ddf867a7794a0e6eb9b3fe59b70c8d9551cf3f411c4902e55a66f7a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16805",
    "kind": "arxiv",
    "version": 1
  }
}