Pith Number

pith:Q4DVLKYK

pith:2026:Q4DVLKYKSV6XYIMQMLNHEF4UBW

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Motion Cues from Image-based Point Tracking for LiDAR Scene Flow Estimation

Gyeongrok Oh, Hyung-gun Chi, Hyunju Ryu, Jonghyun Choi, Jong Wook Kim, Sangpil Kim, SeungHyeon Kim, Seungryong Kim, Youngdong Jang

Dense image trajectories from point tracking refine static-dynamic labels for LiDAR scene flow.

arxiv:2605.16922 v1 · 2026-05-16 · cs.CV

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Claims

C1strongest claim

TrackCue produces more accurate static-dynamic classification and provides more reliable supervision for scene flow learning, as shown by significantly improved precision and F1 score of dynamic labels leading to performance gains.

C2weakest assumption

That dense image-space trajectories from point tracking can be accurately associated with and lifted to corresponding LiDAR points without introducing new errors from calibration, viewpoint differences, or tracking failures in occluded regions.

C3one line summary

TrackCue uses dense image-space trajectories from point tracking and ego-motion compensation to improve static-dynamic classification and supervision for LiDAR scene flow estimation.

References

53 extracted · 53 resolved · 0 Pith anchors

[1] Neural scene flow prior.Advances in Neural Information Processing Systems, 34:7838–7851, 2021 2021

[2] Fast neural scene flow 2023

[3] Uniflow: Towards zero-shot lidar scene flow for autonomous vehicles via cross-domain generalization, 2025 2025

[4] Deltaflow: An efficient multi-frame scene flow estimation method 2026

[5] Neural eulerian scene flow fields 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:30.634945Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

870755ab0a957d7c219062da7217940db2bc02e19a1333fbdb88c5b1bb0c810b

Aliases

arxiv: 2605.16922 · arxiv_version: 2605.16922v1 · doi: 10.48550/arxiv.2605.16922 · pith_short_12: Q4DVLKYKSV6X · pith_short_16: Q4DVLKYKSV6XYIMQ · pith_short_8: Q4DVLKYK

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/Q4DVLKYKSV6XYIMQMLNHEF4UBW \
  | 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: 870755ab0a957d7c219062da7217940db2bc02e19a1333fbdb88c5b1bb0c810b

Canonical record JSON

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    "abstract_canon_sha256": "118518e5927c9eacf5a1ec9848943c97d806aa9900cd566f899eb956a3fde008",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-16T10:28:54Z",
    "title_canon_sha256": "dc4b4fe4b49c711d0c59111fcf9bbb1a89dfdfa61965ae5c2e9913556d214552"
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    "kind": "arxiv",
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