Performance Evaluation of Plane Detection in Markerless Augmented Reality under Variations in Surface Characteristics and Light Intensity

Muhammad Zaidaan Fadhlullah, Mulia Sulistiyono

Abstract


Plane detection is a key component of markerless augmented reality (AR) systems, as it determines the system’s ability to recognize surfaces for virtual object placement. However, plane detection performance is influenced by surface characteristics and lighting intensity, which can affect the quality and stability of feature points during the tracking process. This study aims to evaluate the performance of plane detection in a Unity-based markerless AR system using AR Foundation under varying surface characteristics and lighting intensities. The experiments were conducted using three surface types: highly textured, low-textured, and reflective surfaces, under three lighting conditions: bright, dim, and dark. Each scenario was tested five times using four evaluation parameters: tracking time, detection success rate, drift distance, and coverage area. The results show that highly textured surfaces provided the best overall performance across all evaluation parameters, with the fastest tracking time ranging from 2.82 to 6.66 seconds, a detection success rate of 100%, the lowest drift distance ranging from 0.04 to 1.55 cm, and a wider coverage area of 81.04% compared with the other conditions. In contrast, low-textured surfaces presented the most challenging condition, particularly under dark lighting, which resulted in detection failure across all trials. Meanwhile, reflective surfaces were still able to detect planes in most trials but exhibited reduced performance due to the instability of feature points caused by light reflections. Overall, the findings demonstrate that plane detection performance varies across different combinations of surface characteristics and lighting intensities.

Keywords


lighting conditions; markerless augmented reality; plane detection; unity AR foundation; surface characteristics

Full Text:

PDF

References


F. Zulfiqar, R. Raza, M. O. Khan, M. Arif, A. Alvi, and T. Alam, “Augmented Reality and its Applications in Education: A Systematic Survey,” IEEE Access, Vol. 11, pp. 143250–143271, 2023, DOI: 10.1109/ACCESS.2023.3331218.

Y. Abdurrahman and M. Azrino Gustalika, “Aplikasi augmented reality dengan marker based dan markerless tracking sebagai Pengenalan Budaya CANDI Mendut,” Remik: Riset dan E-Jurnal Manajemen Informatika Komputer, Vol. 7, No. 2, pp. 859–871, Apr. 2023, DOI: 10.33395/remik.v7i2.12137.

K. S. Kartini, N. L. P. Labasariyani, M. I. S. Abenk, and I. N. T. A. Putra, “Analisis Perbandingan Efektivitas Augmented Reality Marker-based dan Markerless pada Media Pembelajaran Struktur Tumbuhan,” Digital Transformation Technology, Vol. 5, No. 1, pp. 301–309, Jul. 2025, DOI: 10.47709/digitech.v5i1.6343.

L. N. Amali, M. R. Katili, A. Dwinanto, and Rasim, “Markerless Versus Marker-based Augmented Reality: Comparative Usability Insights from a Museum Context,” Bulletin of Electrical Engineering and Informatics, Vol. 15, No. 2, pp. 1770–1785, Apr. 2026, DOI: 10.11591/eei.v15i2.11286.

N. El Barhoumi, R. Hajji, Z. Bouali, Y. Ben Brahim, and A. Kharroubi, “Assessment of 3D Models Placement Methods in Augmented Reality,” Applied Sciences (Switzerland), Vol. 12, No. 20, Oct. 2022, DOI: 10.3390/app122010620.

C. Theodorou, V. Velisavljevic, V. Dyo, and F. Nonyelu, “Visual SLAM Algorithms and Their Application for AR, Mapping, Localization and Wayfinding,” Array, Vol. 15, Sep. 2022, DOI: 10.1016/j.array.2022.100222.

A. Pramudwiatmoko and R. Armanda, “Fitur Modifikasi Objek untuk Ketidakakuratan Spasial pada Aplikasi Furnitur Markerless Augmented Reality,” JIKO (Jurnal Informatika dan Komputer), Vol. 9, No. 2, p. 393, Jun. 2025, DOI: 10.26798/jiko.v9i2.1650.

I. Irawati, L. N. Hayati, and M. N. Alfath, “Aplikasi Augmented Reality berbasis Plane Detection untuk Visualisasi Objek Furniture Ruangan,” JIKO (Jurnal Informatika dan Komputer), Vol. 7, No. 2, pp. 244–250, Sep. 2023, DOI: 10.26798/jiko.v7i2.801.

M. A. Maneli and O. E. Isafiade, “A Multifactor Comparative Assessment of Augmented Reality Frameworks in Diverse Computing Settings,” IEEE Access, Vol. 11, pp. 12474–12486, 2023, DOI: 10.1109/ACCESS.2023.3242238.

B. Li, X. Wang, Q. Gao, Z. Song, C. Zou, and S. Liu, “A 3D Scene Information Enhancement Method Applied in Augmented Reality,” Electronics (Switzerland), Vol. 11, No. 24, Dec. 2022, DOI: 10.3390/electronics11244123.

K. C. Brata, N. Funabiki, P. A. Riyantoko, Y. Y. F. Panduman, and M. Mentari, “Performance Investigations of VSLAM and Google Street View Integration in Outdoor Location-based Augmented Reality under Various Lighting Conditions,” Electronics (Switzerland), Vol. 13, No. 15, Aug. 2024, DOI: 10.3390/electronics13152930.

T. Scargill, G. Premsankar, J. Chen, and M. Gorlatova, “Here to Stay: A Quantitative Comparison of Virtual Object Stability in Markerless Mobile AR,” in 2022 2nd International Workshop on Cyber-Physical-Human System Design and Implementation (CPHS), IEEE, 2022, pp. 24–29. DOI: 10.1109/CPHS56133.2022.9804545.

T. Scargill, Y. Chen, N. Marzen, and M. Gorlatova, “Integrated Design of Augmented Reality Spaces using Virtual Environments,” in Proceedings - 2022 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 297–306. DOI: 10.1109/ISMAR55827.2022.00045.

Z. Wu, D. Li, C. Li, Y. Chen, and S. Li, “Feature Point Tracking Method for Visual SLAM based on Multi-Condition Constraints in Light Changing Environment,” Applied Sciences (Switzerland), Vol. 13, No. 12, Jun. 2023, DOI: 10.3390/app13127027.

K. Xu, Y. Hao, S. Yuan, C. Wang, and L. Xie, “AirSLAM: An Efficient and Illumination-Robust Point-Line Visual SLAM System,” IEEE Transactions on Robotics, Vol. 41, pp. 1673–1692, 2025, DOI: 10.1109/TRO.2025.3539171.

Y. Kim and H. Woo, “Integrating a Deep Learning-based Plane Detector in Mobile AR Systems for Improvement of Plane Detection,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Mar. 2022, pp. 597–602. DOI: 10.1145/3532213.3532304.

Y. Kojima, T. Iriyama, and T. Komuro, “AR Digital Workspace using a Mobile Device,” in Proceedings - SUI 2022: ACM Conference on Spatial User Interaction, Association for Computing Machinery, Inc, Dec. 2022. DOI: 10.1145/3565970.3567690.

A. M. Sharifnia, D. E. Kpormegbey, D. K. Thapa, and M. Cleary, “A Primer of Data Cleaning in Quantitative Research: Handling Missing Values and Outliers,” J. Adv. Nurs., Vol. 82, No. 1, pp. 970–975, Jan. 2026, DOI: 10.1111/jan.16908.

E. Barroga et al., “Conducting and Writing Quantitative and Qualitative Research,” J. Korean Med. SCI., Vol. 38, No. 37, p. e291, 2023, DOI: 10.3346/jkms.2023.38.e291.




DOI: https://doi.org/10.32520/stmsi.v15i8.6834

Article Metrics

Abstract view : 1 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.