From Geometric Labels to Semantic Understanding of Indoor Building Components Using Multimodal Large Language Models
Point cloud-based understanding has become an important enabler for facility operation and maintenance involving indoor building components. However, existing methods output only discrete labels without explaining component functions or natural language interactions. This paper proposes Building-MLLM, a point cloud-centered multimodal large language model (MLLM) for indoor components, which models point clouds and instructions to generate responses across Simple Recognition, Complex Captioning, and Multi-Engineering Question Answering tasks. Building-MLLM addresses semantic concentration through four domain-specific mechanisms: Point Information Enhancer for task-relevant semantics, Geome...
| Authors | Shuju Jing, Chao Yin |
|---|---|
| Primary category | cs.CV |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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