We propose an automatic method for fast reconstruction of indoor scenes from raw point scans, which is a fairly challenging problem due to the restricted accessibility and the cluttered space for indoor environment. We first detect and remove points representing the ground, walls and ceiling from the input data and cluster the remaining points into different groups, referred to as sub-scenes. Our approach abstracts the sub-scenes with geometric primitives, and accordingly constructs the topology graphs with structural attributes based on the functional parts of objects (namely, anchors). To decompose sub-scenes into individual indoor objects, we devise an anchor-guided subgraph matching algorithm which leverages template graphs to partition the graphs into subgraphs (i.e., individual objects), which is capable of handling arbitrarily oriented objects within scenes. Subsequently, we present a data-driven approach to model individual objects, which is particularly formulated as a model instance recognition problem. A Randomized Decision Forest (RDF) is introduced to achieve robust recognition on decomposed indoor objects with raw point data. We further exploit template fitting to generate the geometrically faithful model to the input indoor scene. We visually and quantitatively evaluate the performance of our framework on a variety of synthetic and raw scans, which comprehensively demonstrates the efficiency and robustness of our reconstruction method on raw scanned point clouds, even in the presence of noise and heavy occlusions. 
Cluttered indoor scene modeling via functional part-guided graph matching