Chialing Wei

Arizona State University | ASU · Department of Civil, Environmental and Sustainable Engineering

Bike lane width is critical information for civil infrastructure planers to promote city sustainability, health, and urban livability. This research presents a feasibility investigation to develop a workflow for the recognition of bike lane markings using city-scale point cloud data. City-scale point cloud data was collected employing a backpack mo...

We present an automated scan-to-BIM pipeline that simplifies the 3D building object recognition problem into a 2D recognition problem. We used the Habitat Matterport 3D Dataset (HM3D) for training wall detection model. The weakly supervised learning is conducted since we used the noisy depth-projected annotation. We isolated individual building lev...

Current computer vision methods for symbol detection in piping and instrumentation diagrams (P&IDs) face limitations due to the manual data annotation resources they require. This paper introduces a versatile two-stage symbol detection pipeline that optimizes efficiency by (1) labeling only data samples with minimal cumulative informational redunda...

Building owners are working on converting their legacy documentation 2D floor plans into digital 3D representations, but the manual process is labor-intensive and time-consuming. In this paper, deep learning is leveraged to automate the process. This automation requires interoperability between artificial neural networks and prevailing 3D modeling...

For successfully training neural networks, developers often require large and carefully labelled datasets. However, gathering such high-quality data is often time-consuming and prohibitively expensive. Thus, synthetic data are used for developing AI (Artificial Intelligence) /ML (Machine Learning) models because their generation is comparatively fa...

Generating digital 3D buildings models from scratch is time-consuming and labor-intensive. In this paper, we present an automated detection process leveraging computer vision and the information available in 2D drawings to reduce 3D modeling time. The recognition system is limited to walls and has two parts: (1) Image classification on walls by Res...