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所属单位：机电学院
发表刊物：COMPUTER-AIDED DESIGN
关键字：Raw LiDAR data Repetition detection Indoor scene modeling Key point extraction
摘要：Automatic modeling of indoor scenes from raw point data has received considerable attention due to its wide applications in computer graphics and robotics. The raw point data, however, always suffer from incompleteness, noise and anisotropy in density, which make rapid reconstruction fairly challenging. To overcome these challenges, in this paper, we explore the repeatability and regularity of man-made structures, which is the crux of our automatic reconstruction of indoor scenes. As observed, repetitive structures oftentimes exhibit in indoor scenes, such as classrooms, meeting rooms, and auditoria. We detect repetitions hierarchically and extract each repetitive object separately in these scenes. The object is represented with a set of key points which are extracted by leveraging both the local and global information of the data. By retrieving the most similar models from the shape database, we align them with the input data to obtain a high-quality virtual representation of the scene, which is quite faithful to the original geometry. In contrast to previous methods, we discover the high-level structure of the scene and can obtain a complete reconstruction efficiently even in the presence of noise and incomplete scans. A variety of indoor scenes have been tested to verify the effectiveness and the robustness of our proposed method. (C) 2017 Elsevier Ltd. All rights reserved.
ISSN号：0010-4485
是否译文：否
发表时间：2018-01-01
合写作者：吴巧云,Remil, Oussama,Guo, Yanwen,魏明强
通讯作者：汪俊,魏明强