3D Object Classification Using Geometric Features and Pairwise Relationships

Abstract

Object classification is a key differentiator of building information modeling (BIM) from three-dimensional (3D) computer-aided design (CAD). Incorrect object classification impedes the full exploitation of BIM models. Models prepared using domainspecific software cannot ensure correct object classification when transferred to other domains, and research on reconstruction of BIM models using spatial survey has not proved a full capability to classify objects. This research proposed an integrated approach to object classification that applied domain experts' knowledge of shape features and pairwise relationships of 3D objects to effectively classify objects using a tailored matching algorithm. Among its contributions: the algorithms implemented for shape and spatial feature identification could process various complex 3D geometry; the method devised for compilation of the knowledge base considered both rigor and confidence of the inference; the algorithm for matching provides mathematical measurement of the object classification results. The integrated approach has been applied to classify 3D bridge objects in two models: a model prepared using incorrect object types and a model manually reconstructed using point cloud data. All these objects were successfully classified.

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

“…An alternative to the aforementioned methods uses only the logical and unique spatial and geometrical relationships between different object types to semantically label planar segments [9,35]. For instance, a segmented planar surface of an indoor room can be a wall, floor, ceiling, or clutter.…”

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

“…The large number of published articles on MI–BCI tasks using EEG signals highlights the importance of the applicability of EEG signals in the BCI domain (Hwang et al, ; Ortiz‐Rosario and Adeli, ). Most EEG‐based BCI systems usually use a structured approach that includes three main steps: (a) preprocessing that may contain three suboperations of noise removal (NR; Çınar, S., and Acır, ; Mutanen et al, ), channel selection (CS; Ghaemi et al, ; Rathee et al, ), and data augmentation (Kalunga et al, 2015; Krell et al, 2018); (b) feature construction, that is choosing appropriate properties of signals, consists of two suboperations of feature extraction (Hsu, ; Zhang et al, ; Aghaei et al, ; Cai et al, ), and feature selection (Zhang et al, ; Lin et al, ; Ma et al, ); and (c) classification that is performed using an appropriate classifier such as support vector machine (Khedher et al, ; Dai and Cao, ; Direito et al, ), probabilistic neural networks (Adeli and Panakkat, ; Sankari and Adeli, ), enhanced probabilistic neural network (Ahmadlou and Adeli, ; Hirschauer et al, ; Fernandes et al, ), competitive probabilistic neural network (Zeinali and Story, ), the recently developed neural dynamics classification algorithm (Rafiei and Adeli, ), or a combination or ensemble of classifiers (Oliveira‐Santos et al ; Reyes et al ). It should be noted that the necessity of each aforementioned suboperation is usually determined by a BCI expert, which is not convenient in practice.…”

“…choosing appropriate properties of signals, consists of two suboperations of feature extraction (Hsu, 2015;Zhang et al, 2015;Aghaei et al, 2016;Cai et al, 2017), and feature selection Lin et al, 2017;Ma et al, 2018); and (c) classification that is performed using an appropriate classifier such as support vector machine (Khedher et al, 2017;Dai and Cao, 2017;Direito et al, 2017), probabilistic neural networks (Adeli and Panakkat, 2009;Sankari and Adeli, 2011), enhanced probabilistic neural network (Ahmadlou and Adeli, 2010;Hirschauer et al, 2015;Fernandes et al, 2016), competitive probabilistic neural network (Zeinali and Story, 2017), the recently developed neural dynamics classification algorithm (Rafiei and Adeli, 2017), or a combination or ensemble of classifiers (Oliveira-Santos et al 2018;Reyes et al 2018). It should be noted that the necessity of each aforementioned suboperation is usually determined by a BCI expert, which is not convenient in practice.…”

Detection of Structural Components in Point Clouds of Existing RC Bridges

“…It is difficult to generalize this algorithm to large RC bridges, as the real point clouds usually suffer from occlusions and nonuniformly distributed points. Ma et al (2017) leverage relationship knowledge and shape features to classify bridge 3D solid objects. First, the input of this method needs to be a solid bridge model (i.e., not a bridge point cloud).…”

“…Ma et al. () leverage relationship knowledge and shape features to classify bridge 3D solid objects. First, the input of this method needs to be a solid bridge model (i.e., not a bridge point cloud).…”