Real Time Detection of Repeated Structures in Point Clouds of Urban Scenes

Sam Friedman, Ioannis Stamos

Abstract: Laser range scanners provide rich 3D representations of urban scenes. These scenes are replete with repetitive architectural features such as windows, balconies, and cornices. Processing of dense 3D images is often computationally intensive and occurs offline after acquisition. Here, however, we present an online algorithm for the detection of repetitive features in 3D range scans. Our algorithm creates a function from each scan line by deriving a local measure at each point. Computing the Fourier transform of that function reveals the periodicity of the scene. This robust algorithm forms the basis for novel methods of feature extraction, compression, and registration. What is more this whole process can be executed on-the-fly and integrated into hardware transforming laser scanners into architecture aware devices.