Pith Number

pith:JJFNVWUN

pith:2026:JJFNVWUNLLAPOI2YZEGOB3J5QJ

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Analogical Trajectory Transfer

Eun Sun Lee, Gwangtak Bae, Junho Kim, Seunggu Kang, Young Min Kim

Scenes are partitioned into object-centric clusters whose cross-scene mappings are predicted hierarchically from 3D foundation features and then assembled and refined to transfer trajectories while preserving semantics and avoiding clashes.

arxiv:2605.14393 v1 · 2026-05-14 · cs.CV

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Claims

C1strongest claim

We partition scenes into object-centric clusters and estimate cross-scene mappings via hierarchical smooth map prediction, using 3D foundation model features that encode contextual information from object and open-space arrangements. We then combinatorially assemble the per-cluster maps into an initial transfer and refine the result to remove collisions and distortions.

C2weakest assumption

That 3D foundation model features provide sufficient contextual information to produce accurate cross-scene mappings that preserve both semantics and functionality without requiring scene-specific training or manual tuning.

C3one line summary

A method transfers trajectories across 3D scenes by clustering objects, predicting hierarchical smooth maps from foundation model features, assembling them combinatorially, and refining for coherence.

References

192 extracted · 192 resolved · 0 Pith anchors

[1] Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , month = 2021

[2] The Farthest Point Strategy for Progressive Image Sampling , volume =

[3] Eddy, William F. , title =. 1977 , issue_date =. doi:10.1145/355759.355766 , journal = 1977 · doi:10.1145/355759.355766

[4] Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , year=

[5] International Journal of Computer Vision (IJCV) , pages=

Formal links

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Receipt and verification

First computed	2026-05-17T23:39:07.593205Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4a4adada8d5ac0f72358c90ce0ed3d8260536dd67f11423011cd1fc1ab7407d6

Aliases

arxiv: 2605.14393 · arxiv_version: 2605.14393v1 · doi: 10.48550/arxiv.2605.14393 · pith_short_12: JJFNVWUNLLAP · pith_short_16: JJFNVWUNLLAPOI2Y · pith_short_8: JJFNVWUN

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JJFNVWUNLLAPOI2YZEGOB3J5QJ \
  | 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: 4a4adada8d5ac0f72358c90ce0ed3d8260536dd67f11423011cd1fc1ab7407d6

Canonical record JSON

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    "abstract_canon_sha256": "7a882010299427928bca18a6c637778731d241cd60899ce741b6b07912b6385a",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T05:14:59Z",
    "title_canon_sha256": "b3c68291306b9afd8f0fd2fa1626bd09fda01c0b10058bc0013c307dfc73faa4"
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    "kind": "arxiv",
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