Slice-Guided Components Detection and Spatial Semantics Acquisition of Indoor Point Clouds

Abstract

Extracting indoor scene components (i.e., the meaningful parts of indoor objects) and obtaining their spatial relationships (e.g., adjacent, in the left of, etc.) is crucial for scene reconstruction and understanding. At present, the detection of indoor scene components with complex shapes is still challenging. To fix the problem, a simple yet powerful slice-guided algorithm is proposed. The key insight is that slices of indoor scene components always have similar profiles no matter if the components are simple-shaped or complex-shaped. Specifically, we sliced the indoor scene model into many layers and transformed each slice into a set of two-dimensional (2D) profiles by resampling. After that, we clustered 2D profiles from neighbor slices into different components on the base of spatial proximity and similarity. To acquire the spatial relationships between indoor scene components, an ontology was constructed to model the commonsense knowledge about the semantics of indoor scene components and their spatial relationships. Then the spatial semantics of the relationships between indoor scene components were inferred and a semantic graph of spatial relationship (SGSR) was yielded to represent them. The experimental results demonstrate that our method can effectively detect complex-shaped indoor scene components. The spatial relationships between indoor components can be exactly acquired as well.