Building Model Retrieval System for Automatic City Modeling An increasing number of three-dimensional (3D) building models are being made available on Web-based model-sharing platforms. Based on the concept of data reuse, an automatic 3D building model retrieval system is created to query the similar models by using point clouds which acquired by airborne light detection and ranging (LiDAR) systems. To encode LiDAR point clouds with sparse, noisy, and incomplete sampling, a novel encoding scheme is introduced based on a set of low-frequency spherical harmonic basis functions. These functions provide compact representation and ease the encoding difficulty coming from inherent noises of point clouds. Additionally, a data filling and resampling technique is proposed to solve the aliasing problem caused by the sparse and incomplete sampling of point clouds.
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