Pith Number

pith:C7I2UO7Y

pith:2026:C7I2UO7YMZ2K5DFDOKE42F3UBA

not attested not anchored not stored refs resolved

Generalizable and Actionable Parts Pose Estimation with Symmetry Annotation-Free Learning Strategy

Dan Guo, Di Wu, Liu Liu, Wenxiao Chen, Xueyu Yuan

Self-supervised symmetry modeling enables annotation-free pose estimation for object parts across categories.

arxiv:2605.17033 v1 · 2026-05-16 · cs.RO

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\pithnumber{C7I2UO7YMZ2K5DFDOKE42F3UBA}

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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

We propose SAFAG, a novel Symmetry Annotation-Free framework for Generalizable and Actionable Parts Pose Estimation. Specifically, we suggest a stepwise refinement two-stage framework for candidate-to-final quaternion regression, and tackle the symmetry prediction as a probability distribution problem with self-supervised learning strategy.

C2weakest assumption

That self-supervised learning on symmetry as a probability distribution, combined with the two-stage candidate-to-final regression, will produce accurate and generalizable pose estimates without any symmetry annotations or rich labeled data.

C3one line summary

SAFAG introduces a symmetry annotation-free two-stage learning strategy for generalizable actionable parts pose estimation in robotics.

References

35 extracted · 35 resolved · 1 Pith anchors

[1] arXiv preprint arXiv:1711.00199 (2017) 23 · arXiv:1711.00199

[2] Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

[3] Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

[4] 2023 IEEE International Conference on Robotics and Automation (ICRA) , pages= 2023

[5] Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Receipt and verification

First computed	2026-05-20T00:03:36.880008Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

17d1aa3bf86674ae8ca37289cd1774081351549c0b37baa276d74e4c85312639

Aliases

arxiv: 2605.17033 · arxiv_version: 2605.17033v1 · doi: 10.48550/arxiv.2605.17033 · pith_short_12: C7I2UO7YMZ2K · pith_short_16: C7I2UO7YMZ2K5DFD · pith_short_8: C7I2UO7Y

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/C7I2UO7YMZ2K5DFDOKE42F3UBA \
  | 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: 17d1aa3bf86674ae8ca37289cd1774081351549c0b37baa276d74e4c85312639

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "09792ca644ad3725a552c0ad265fdd775791f3457e41edf0d092eccbbdf5d2e4",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-16T15:05:32Z",
    "title_canon_sha256": "deb43ca799c66d787e41bd1fef2d63ddf8bdfc64a3b6115b1430ca9c48270b3a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17033",
    "kind": "arxiv",
    "version": 1
  }
}