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User exemplar-based building element retrieval from raw point clouds using deep point-level features
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DOI：10.1016/j.autcon.2020.103159.png)
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Abstract
En 中文
3D point cloud data can be utilized for site inspection and reverse engineering of building models. However, conventional methods for building element retrieval require a database of 3D CAD or BIM models which are unsuitable for the case of historical buildings without as-planned models or temporary structures that are not in the pre-built model. Thus, this paper proposes a semi-automated method to efficiently retrieve duplicate building elements without these constraints. First, the point cloud is processed with a pre-trained deep feature extractor to generate a 50-dimensional feature vector for each point. Next, the point cloud is segmented through feature clustering and region-growing algorithms, then displayed on a user interface for selection. Lastly, the selected exemplar is provided as input to a peak-finding algorithm to determine positive matches. Experimental results on five different datasets show that the proposed method obtains average rates above 90% for precision and recall.
Keywords:
Laser scanning
Point cloud
Building element retrieval
Machine learning
Deep neural network
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