Abstract
Abstract. With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.
Highlights
Recent development in modeling and scanning techniques has led to an increasing number of 3D models
This study aims at the efficient construction of a cyber city by encoding unorganized, noisy, and incomplete building point clouds acquired by airborne Light Detection and Ranging (LiDAR), as well as by retrieving 3D building models from model databases or from the Internet
A point cloud is encoded by geometric features of its depth image, which has the properties of rotation-invariance and noiseinsensitivity
Summary
Recent development in modeling and scanning techniques has led to an increasing number of 3D models. This study aims at the efficient construction of a cyber city by encoding unorganized, noisy, and incomplete building point clouds acquired by airborne LiDAR, as well as by retrieving 3D building models from model databases or from the Internet. Most previous studies focus on encoding and retrieving 3D polygon models using polygon models as input queries (Funkhouser et al, 2003; Assfalg et al, 2007; Gao et al, 2011; Akgul et al, 2009; Gao et al, 2012) These studies do not consider model retrieval by using point clouds, which is in a great need in the topic of efficient cyber city construction with airborne LiDAR point clouds. The proposed depth image encoding method reduces data description dimensions and yields a compact shape descriptor, resulting in both storage size and search time reduction
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