pith:FWUC24DS
Coarse Semantic Injection for LLM-Conditioned Structured Indoor Prediction
Appending a coarse four-group semantic color code to raw point attributes before tokenization improves LLM-based structured indoor prediction while leaving the decoder unchanged.
arxiv:2605.16832 v1 · 2026-05-16 · cs.CV
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Record completeness
Claims
The semantic color code is appended to the original raw point attributes before tokenization, so geometry and semantics share the same sparse tokenization path while the downstream language model decoder and output serialization remain unchanged.
That reliable coarse semantic evidence (furniture/walls/openings/others) can be obtained from RGB or other sources and injected without introducing errors that outweigh the benefits after sparse pooling and LLM decoding.
Coarse four-group semantic color coding (RGBB) appended to point clouds before tokenization improves LLM-based structured indoor prediction on Structured3D, SpatialLM, and ARKitScenes, especially for openings and furniture instances.
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Receipt and verification
| First computed | 2026-05-20T00:03:25.096709Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2da82d707214bb6e12db0c044d15562c04dc1875b7f1195661121332e9d3100d
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FWUC24DSCS5W4EW3BQCE2FKWFQ \
| 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: 2da82d707214bb6e12db0c044d15562c04dc1875b7f1195661121332e9d3100d
Canonical record JSON
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