Learning-based 6 DOF Camera Pose Estimation Using BIM-generated Virtual Scene for Facility Management
Journal
Proceedings of the International Symposium on Automation and Robotics in Construction
Start Page
42
End Page
48
ISSN
24135844
ISBN (of the container)
9780645832228
Date Issued
2025
Author(s)
Abstract
Image-based indoor localization is a promising approach to enhancing facility management efficiency. However, ensuring localization accuracy and improving data accessibility remain key challenges. Therefore, this research aims to automatedly localize images captured during facility inspections by matching the viewpoint of the camera with a corresponding viewpoint in a Building Information Modeling-based (BIM) simulated environment. In this paper, we present a framework that generates photorealistic synthetic images and trains a deep learning model for camera pose estimation. Synthetic datasets are generated in a simulation environment, allowing precise control over scene parameters, camera positions, and lighting conditions. This allows the creation of diverse and realistic training data tailored to specific facility environments. The deep learning model takes RGB images, semantic segmented maps, and corresponding camera poses as inputs to predict six-degree-of-freedom (6DOF) camera poses, including position and orientation. Experimental results demonstrate that the proposed approach can enable indoor image localization with an average translation error of 5.8 meters and a rotation error of 69.05 degrees.
Event(s)
42nd International Symposium on Automation and Robotics in Construction, ISARC 2025, Montreal, 28 July 2025 - 31 July 2025
Subjects
BIM
Facility management
Image localization
Pose estimation
Synthetic data
Publisher
International Association for Automation and Robotics in Construction (IAARC)
Type
conference paper