pith:W7EVYY73
TTT3R: 3D Reconstruction as Test-Time Training
Framing 3D reconstruction as test-time training yields a closed-form learning rate from alignment confidence that doubles global pose accuracy on long sequences.
arxiv:2509.26645 v4 · 2025-09-30 · cs.CV
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Claims
This training-free intervention, termed TTT3R, substantially improves length generalization, achieving a 2× improvement in global pose estimation over baselines, while operating at 20 FPS with just 6 GB of GPU memory to process thousands of images.
That alignment confidence between the memory state and incoming observations can be computed reliably and directly yields a closed-form learning rate that correctly balances retention of history with adaptation to new data without introducing instability or bias.
TTT3R derives a closed-form learning rate from memory-observation alignment confidence to boost length generalization in RNN-based 3D reconstruction by 2x in global pose estimation.
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| First computed | 2026-05-17T23:38:14.802949Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b7c95c63fb0260f885d03003e57c83690a56bdacde9bc816fbfbd059b4ca1e07
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/W7EVYY73AJQPRBOQGAB6K7EDNE \
| 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())"
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Canonical record JSON
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