Coarse Semantic Injection for LLM-Conditioned Structured Indoor Prediction
Large language models (LLMs) have recently been used as structured decoders for indoor understanding from 3D point-token inputs. However, point cloud encoders often under-represent thin structural elements such as doors and windows after voxelization and sparse pooling, and may miss individual furniture instances in cluttered scenes. We propose an interface-preserving semantic augmentation for LLM-conditioned structured decoding. The key idea is to associate semantic evidence with the point-cloud representation, reduce it to a coarse four-group code (furniture, walls, openings, and others), and encode it as an RGBB point interface: red for furniture, green for walls, blue for openings, an...
| Authors | Shuliang Zhu, Tomiwa Adey, Jinjia Zhou |
|---|---|
| Primary category | cs.CV |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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