Pith Number

pith:733ISBBT

pith:2026:733ISBBTU3CIIRP5HAP62GRU25

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LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields

Ayoung Kim, Dongjae Lee, Maurice Fallon, Wooseong Yang, Yifu Tao

LAPS manages replay buffers with reliability-based pooling and uncertainty sampling to reduce forgetting in incremental neural LiDAR mapping.

arxiv:2605.15496 v1 · 2026-05-15 · cs.RO · cs.CV

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Claims

C1strongest claim

LAPS combines reliability-based active pooling to retain reliable historical samples under limited memory with uncertainty-guided active sampling to focus optimization on under-constrained regions, consistently improving reconstruction completeness while maintaining competitive geometric accuracy.

C2weakest assumption

That reliability scores and uncertainty estimates computed from the current neural distance field provide unbiased signals for which samples to retain and which regions to prioritize, without the selection process itself degrading map quality or introducing systematic bias in the replay buffer.

C3one line summary

LAPS improves incremental neural LiDAR mapping by combining reliability-based active pooling for sample retention with uncertainty-guided active sampling for optimization focus.

References

27 extracted · 27 resolved · 1 Pith anchors

[1] V . Reijgwart, J. Behley, T. Vidal-Calleja, H. Oleynikova, L. Ott, C. Stachniss, and A. Kim, “Dense map representation,” in SLAM Handbook. From Localization and Mapping to Spatial Intelligence, L. Car 2026

[2] Shine-mapping: Large-scale 3d mapping using sparse hierarchical implicit neural representations, 2023

[3] Neural geometric level of detail: Real-time rendering with implicit 3d shapes, 2021

[4] Instant neural graphics primitives with a multiresolution hash encoding, 2022

[5] Pin-slam: Lidar slam using a point-based implicit neural representation for achieving global map consistency, 2024

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Receipt and verification

First computed	2026-05-20T00:01:01.697142Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

fef6890433a6c48445fd381fed1a34d763f30d34789253d44aac8e7a3094200a

Aliases

arxiv: 2605.15496 · arxiv_version: 2605.15496v1 · doi: 10.48550/arxiv.2605.15496 · pith_short_12: 733ISBBTU3CI · pith_short_16: 733ISBBTU3CIIRP5 · pith_short_8: 733ISBBT

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/733ISBBTU3CIIRP5HAP62GRU25 \
  | 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: fef6890433a6c48445fd381fed1a34d763f30d34789253d44aac8e7a3094200a

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-15T00:22:31Z",
    "title_canon_sha256": "65c71c7d10533e60a2c31f0d16375202dc5dd5f7184e0977b638e932def10706"
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