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pith:AYPRRZTV

pith:2026:AYPRRZTVCTUAZPXDP3BSMYXHR3

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OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation

Ao Liang, Ben Fei, Dekai Zhu, Lingdong Kong, Runnan Chen, Tongliang Liu, Wanli Ouyang, Weidong Yang, Xiang Xu, Xin Li, Yang Wu, Youquan Liu

A single text-conditioned diffusion model generates realistic LiDAR scans across eight domains spanning weather, sensors, and platforms.

arxiv:2605.13815 v1 · 2026-05-13 · cs.CV · cs.RO

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Claims

C1strongest claim

OmniLiDAR generates LiDAR scans in a shared range-image representation across eight representative domains with strong generation fidelity and consistent gains in downstream use cases including generative data augmentation for LiDAR semantic segmentation and 3D object detection.

C2weakest assumption

That mixing domains within each mini-batch combined with text conditioning and the proposed CDFM and DAFS modules enables effective unified training without needing domain-isolated optimization or suffering from negative transfer across heterogeneous shifts.

C3one line summary

A unified text-conditioned diffusion model generates high-fidelity LiDAR scans across eight domains spanning weather, sensor, and platform shifts using cross-domain training and feature modeling.

References

106 extracted · 106 resolved · 3 Pith anchors

[1] UniSeg: A unified multi-modal LiDAR segmentation network and the OpenPCSeg codebase, 2023

[2] Deep learning for LiDAR point clouds in autonomous driving: A review, 2021

[3] LoGoNet: Towards accurate 3D object detection with local-to-global cross-modal fusion, 2023

[4] Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation 2026 · arXiv:2604.18486

[5] Worldlens: Full-spectrum evaluations of driving world models in real world 2025

Cited by

1 paper in Pith

Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis

Receipt and verification

First computed	2026-05-18T02:44:15.347871Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

061f18e67514e80cbee37ec32662e78ec34abd37f3861c330ed2876b91656214

Aliases

arxiv: 2605.13815 · arxiv_version: 2605.13815v1 · doi: 10.48550/arxiv.2605.13815 · pith_short_12: AYPRRZTVCTUA · pith_short_16: AYPRRZTVCTUAZPXD · pith_short_8: AYPRRZTV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/AYPRRZTVCTUAZPXDP3BSMYXHR3 \
  | 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: 061f18e67514e80cbee37ec32662e78ec34abd37f3861c330ed2876b91656214

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7237a5f4a0d93f295571dd15f5274c0d77f4a7012419eb2ed11f4718d7ca3433",
    "cross_cats_sorted": [
      "cs.RO"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T17:42:20Z",
    "title_canon_sha256": "d61e5ec442086eff9e82a609143aee3e92760217357f9c3713c115d0655e54fc"
  },
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  "source": {
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    "kind": "arxiv",
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}