Extraction of indoor objects based on the exponential function density clustering model

Indoor point cloud includes wall, ceiling, floor and many other indoor objects. Extraction of wall, ceiling, floor and many objects in the room is the key for many applications including object identification, facility management and reconstruction of building. In view of this, this paper uses exponential function to construct density clustering model according to the local density within cutoff distance. First, the distances between boundary and indoor point cloud are used to construct the constraint condition of wall density clustering. Second, ceiling and floor are extracted according to the density clustering of z direction and local density model. Third, the distance delta(i) is determined according to the size of local density. Simultaneously, we find that the cluster centers are recognized as points for which the product value of the local density and distance delta(i) is anomalously large. Finally, the cluster belonging of each point to the cluster center is determined according to the distance between each point and cluster center. Finally, indoor objects extraction is achieved by judging the distance between neighboring clusters. We conduct the extraction of indoor objects of different type scenes and compare with clustering and deep learning methods. Comparison results show that the proposed method is superior to the clustering and deep learning methods when adjacent objects are not close to each other, but inferior to the deep learning methods when they are next to each other. Additionally, the extraction accuracy, recall and F1-score are calculated according to the matching rate, TP, FP and FN. It illustrates that the performance of the proposed method is affected by the degree of closeness between objects. (C) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a method for indoor object extraction based on density clustering model. It constructs the constraint condition of wall density clustering using distances, extracts ceiling and floor based on z direction and local density model, and determines cluster centers based on the product value of local density and distance. Indoor object extraction is achieved by judging the distance between neighboring clusters.