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Coarse Semantic Injection for LLM-Conditioned Structured Indoor Prediction

Jinjia Zhou, Shuliang Zhu, Tomiwa Adey

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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Claims

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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.

References

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[1] Proceedings of the IEEE conference on computer vision and pattern recognition , pages=
[2] Wang, Shuzhe and Leroy, Vincent and Cabon, Yohann and Chidlovskii, Boris and Revaud, Jerome , booktitle=
[3] Grounding image matching in 2024
[4] Wang, Jianyuan and Chen, Minghao and Karaev, Nikita and Vedaldi, Andrea and Rupprecht, Christian and Novotny, David , booktitle=
[5] Nie, Yinyu and Han, Xiaoguang and Guo, Shihui and Zheng, Yujian and Chang, Jian and Zhang, Jian Jun , booktitle=

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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

arxiv: 2605.16832 · arxiv_version: 2605.16832v1 · doi: 10.48550/arxiv.2605.16832 · pith_short_12: FWUC24DSCS5W · pith_short_16: FWUC24DSCS5W4EW3 · pith_short_8: FWUC24DS
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Canonical record JSON
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