PUBLICATIONS

Change Component Identification of BIM Models for Facility Management based on Time-variant BIMs or Point Clouds

2023, Mar.

Automation in Construction 147 (2023): 104731

(SCIE, IF: 10.517, Rank: 1/138)

https://doi.org/10.1016/j.autcon.2022.104731

This study proposes a common framework for both Scan-vs-BIM and BIM-vs-BIM to realize change detection for updating BIM geometric models. The framework can be carried out for point cloud or BIM datasets, in which the framework conveys threefold messages of inspected components between query and reference data: locations, semantic labels, and changing states, namely missing, moving, newly added, existing, and uncertain items.

Learning-guided Point Cloud Vectorization for Building Component Modeling

2021, Dec.

Automation in Construction 132 (2021): 103978.

(SCIE, IF: 10.517, Rank: 1/138)

https://doi.org/10.1016/j.autcon.2021.103978

This study presents a novel learning-guided point cloud vectorization to form the vector models of building components.

Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN

2020, Jun.

Remote Sensing 12.12 (2020): 1908.

(SCIE, IF: 5.349, Rank: 30/202)

https://doi.org/10.3390/rs12121908

This paper presents a scheme to conduct moving object recognition with three-dimensional (3D) observation using faster region-based convolutional neural network (Faster R-CNN) with a stationary and rotating Pan Tilt Zoom (PTZ) camera and close-range photogrammetry

Learning and SLAM based Decision Support Platform for Sewer Inspection

2020, Mar.

Remote Sensing 12.6 (2020): 968.

(SCIE, IF: 5.349, Rank: 30/202)

https://doi.org/10.3390/rs12060968

This study developed a compact platform for sewer inspection, which consisted of low-cost components such as infrared and depth cameras with a g-sensor. Except for visual inspection, the platform not only identifies internal faults and obstacles but also evaluates their geometric information, geo-locations, and the block ratio of a pipeline in an automated fashion.

Pavement performance monitoring and anomaly recognition based on crowdsourcing spatiotemporal data

2019, Oct.

Automation in Construction 106 (2019): 102882.

(SCIE, IF: 10.517, Rank: 1/138)

https://doi.org/10.1016/j.autcon.2019.102882

This paper proposes a participatory system for pavement performance monitoring of a country-wide road network based on crowdsourcing spatiotemporal data.

Traffic Sign Detection and Positioning using a Monocular Camera

2019, Sep.

Journal of the Chinese Institute of Engineers 42.8 (2019): 757-769.

(SCIE, IF: 1.107, Rank: 93/175)

https://doi.org/10.1080/02533839.2019.1660220

This study applies a low-cost single-camera system to locate and identify traffic signs and reflects their on-site conditions for assisting in maintenance.

Dense Stereo Matching With Edge-Constrained Penalty Tuning

2018, May.

IEEE Geoscience and Remote Sensing Letters 15.5 (2018): 664-668.

(SCIE, IF: 5.343, Rank: 11/87)

10.1109/LGRS.2018.2805916

This letter presents a gradual SGM cost aggregation that comprises a penalty tuning process. To be more specific, we propose an additional penalty parameter and a weighting formula to handle edge pixels with depth variations, acquiring satisfactory depth estimation by preserving sharp geometric edges and maintaining smoothness without raising extra noise.

Rectified Feature Matching for Spherical Panoramic Images

2018, Jan.

Photogrammetric Engineering & Remote Sensing 84.1 (2018): 25-32.

(SCIE, IF: 1.469, Rank: 168/202)

https://doi.org/10.14358/PERS.84.1.25

In this paper, we present an effective strategy for tackling the problem of distortion to improve the performance of spherical image matching.

Application of laser scanning for rapid geologic documentation of trench exposures.

2017, Jun.

Engineering Geology 224 (2017): 97-104.

(SCIE, IF: 6.902, Rank: 1/41)

https://doi.org/10.1016/j.enggeo.2017.05.010

In this paper, we describe a less costly, novel technique to enhance the quality and rapidity of geologic trench documentation.

Multi-Feature Registration of Point Clouds.

2017, Mar.

Remote Sensing 9.3 (2017): 281.

(SCIE, IF: 5.349, Rank: 30/202)

https://doi.org/10.3390/rs9030281

This paper proposes a multi-feature registration scheme suitable for utilizing point, line, and plane features extracted from raw point clouds to realize the registrations of scans acquired within the same LIDAR system or across different platforms.