Abstraction and Understanding of Indoor Scenes from Single-View RGB-D Scanning Data

The recent development of consumer-level RGB-D sensors increases the acquisition of indoor scanning data. However, the point clouds acquired by such low-quality equipment are usually noisy and incomplete. This paper presents an automatic algorithm for indoor scenes primitive abstraction and segmentation from single-view RGB-D scanning data. The first step is to preprocess the scanning data, which will automatically detect indoor structures such as walls, ceilings and floors. The input data is down-sampled and outliers are filtered out. The remaining point clouds are then abstracted in a semantic-aware manner and be represented by a set of geometric primitives. This abstraction step will produce the robust representation of indoor objects and parts, and reduce the computational cost of processing large-scale data efficiently. Finally, to yield the semantic indoor scene segmentation, a graph-based algorithm is adopted to group these primitives which incorporates feature descriptors and pair-wise geometric relations. Experimental results demonstrate that our proposed approach can semantically segment the cluttered indoor scenes efficiently and robustly.