Recognizing Point Clouds Using Conditional Random Fields

Abstract

Abstract-Detecting objects in cluttered scenes is a necessary step for many robotic tasks and facilitates the interaction of the robot with its environment. Because of the availability of efficient 3D sensing devices as the Kinect, methods for the recognition of objects in 3D point clouds have gained importance during the last years. In this paper, we propose a new supervised learning approach for the recognition of objects from 3D point clouds using Conditional Random Fields, a type of discriminative, undirected probabilistic graphical model. The various features and contextual relations of the objects are described by the potential functions in the graph. Our method allows for learning and inference from unorganized point clouds of arbitrary sizes and shows significant benefit in terms of computational speed during prediction when compared to a state-of-the-art approach based on constrained optimization.

Point Cloud vs. Mesh Features for Building Interior Classification

“…In addition to these transformation invariant features, it is common in building component detection to compute several absolute metrics from the segments. Popular features include the segment's orientation in relation to the gravity or Z-axis, surface area, dimensions and the aspect ratio [4,5,11,[62][63][64][65]. The most frequently used features are summarized in the right column of Table 1.…”