  Over the years, scene understanding has attracted a growing interest in
computer vision, providing the semantic and physical scene information
necessary for robots to complete some particular tasks autonomously. In 3D
scenes, rich spatial geometric and topological information are often ignored by
RGB-based approaches for scene understanding. In this study, we develop a
bottom-up approach for scene understanding that infers support relations
between objects from a point cloud. Our approach utilizes the spatial topology
information of the plane pairs in the scene, consisting of three major steps.
1) Detection of pairwise spatial configuration: dividing primitive pairs into
local support connection and local inner connection; 2) primitive
classification: a combinatorial optimization method applied to classify
primitives; and 3) support relations inference and hierarchy graph
construction: bottom-up support relations inference and scene hierarchy graph
construction containing primitive level and object level. Through experiments,
we demonstrate that the algorithm achieves excellent performance in primitive
classification and support relations inference. Additionally, we show that the
scene hierarchy graph contains rich geometric and topological information of
objects, and it possesses great scalability for scene understanding.
