  Understanding the structural organisation of 3D indoor scenes in terms of
rooms is often accomplished via floorplan extraction. Robotic tasks such as
planning and navigation require a semantic understanding of the scene as well.
This is typically achieved via object-level semantic segmentation. However,
such methods struggle to segment out topological regions like "kitchen" in the
scene. In this work, we introduce a two-step pipeline. First, we extract a
topological map, i.e., floorplan of the indoor scene using a novel
multi-channel occupancy representation. Then, we generate CLIP-aligned features
and semantic labels for every room instance based on the objects it contains
using a self-attention transformer. Our language-topology alignment supports
natural language querying, e.g., a "place to cook" locates the "kitchen". We
outperform the current state-of-the-art on room segmentation by ~20% and room
classification by ~12%. Our detailed qualitative analysis and ablation studies
provide insights into the problem of joint structural and semantic 3D scene
understanding.
