Browse > Article
http://dx.doi.org/10.7583/JKGS.2021.21.5.3

Constructing AR Game Space through Cuboid Detection in Indoor Environment  

Kim, Ki-Sik (Dept. of Computer Science and Engineering, Incheon National University)
Park, Jong-Seung (Dept. of Computer Science and Engineering, Incheon National University)
Publication Information
Journal of Korea Game Society / v.21, no.5, 2021 , pp. 3-16 More about this Journal
Abstract
In this paper, we propose a method of constructing AR game spaces through cuboid detection in indoor environment. Conventional spatial recognition methods can detect planes only in limited spaces that can be well observed. They are also vulnerable in density and noise. The proposed method overcomes the limitations of the conventional method by constructing AR game spaces by a method of detecting OBBs from spherical videos. Experimental results showed that the proposed method is faster than the conventional method and it is also robust against environmental constraints such as changes in density and noisy.
Keywords
Augmented Reality; Point Cloud; Game Space; Cuboid;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. S. Hassanein, S. Mohammad, M. Sameer, M. E. Ragab, "A Survey on Hough Transform, Theory, Techniques and Applications", International Journal of Computer Science, Vol. 12, Issue 1, pp. 1-18., 2015.
2 S. A. Hojjatoleslami, J. Kittler, "Region Growing: A New Approach," IEEE Trans. Image Processing, Vol. 7, Issue 7, pp. 1079-1084, 1998.   DOI
3 R. Schnabel, R. Wahl, R. Klein, "Efficient RANSAC for Point-Cloud Shape Detection", Computer Graphics Forum, Vol. 26, No. 2, pp. 214-226, 2007.   DOI
4 U. Bauer, K. Polthier, "Detection of Planar Regions in Volume Data for Topology Optimization", Lecture Notes in Computer Science 4975, pp. 119-126, 2008.
5 J. Poppinga, N. Vaskevicius, A. Birk, K. Pathak, "Fast Plane Detection and Polygonalization in noisy 3D Range Images", In IROS '08, pp. 3378-3383, 2008.
6 M. Hulik, M. Spanel, P. Smrz, Z. Mateme, "Continuous plane detection in point-cloud data based on 3D Hough Transform", J. Visual Commun. Image Represent., Vol. 25, Issue 1, pp. 86-97, 2014.   DOI
7 Lin Li, Fan Yang, Haihong Zhu, Dalin Li, You Li, Lei Tang, "An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells", Remote Sensing, Vol. 9, No. 5, 2017.
8 P. Biber, W. Strasser, "The Normal Distributions Transform: A New Approach to Laser Scan Matching", in IEEE/RJS Intern. Conf. on Intelligent Robots and Systems, Vol. 3, pp. 2743-2748, 2003.
9 T. El-Ganainy, M. Hefeeda, "Streaming virtual reality content", arXiv:1612.08350, 2016.
10 N. Tayem, H. M. Kwon, "L-Shape 2-Dimensional Arrival Angle Estimation with Propagator Method", IEEE Trans. Antennas Propag., Vol. 53, No. 5, pp. 1622-1630, 2005.   DOI
11 D. Dwibedi, T. Malisiewicz, V. Badrinarayanan, A. Rabinovich, "Deep Cuboid Detection: Beyond 2D Bounding Boxes", arXiv:1611.10010, 2006.
12 S. Fidler, S. Dickinson, R. Urtasun, "3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model", In NIPS, 2012.
13 J. O'Rourke, "Finding Minimal Enclosing Boxes," Int. J. Parallel Programming, Vol. 14, No. 3, 1985.
14 Abner M. C. Araujo, Manuel M. Oliveira, "A Robust Statistics Approach for Plane Detection in Unorganized Point Clouds," Pattern Recognition, Vol. 100, pp. 1-12, 2020.
15 B. Naujoks, H. J. Wuensche, "An Orientation Corrected Bounding Box Fit Based on the Convex Hull under Real Time Constraints," In Proc. IEEE Int. Vehicles Symp., pp. 415-420, 2018.
16 R. V. Chadnov, A. V. Skvortsov, "Convex hull algorithms review", In Proc. 8th Russian-Korean Int. Symp. on Science and Technology, Vol. 2, pp. 112-115, 2004.
17 Z. S. Hashemifar, K. W. Lee, N. Napp, and K. Dantu, "Consistent Cuboid Detection for Semantic Mapping", In Proc. IEEE 11th ICSC, 2017.
18 S. Sumikura, M. Shibuya, K. Sakurada, "OpenVSLAM: A Versatile Visual SLAM Framework," In Proc. 27th ACM Int. Conf. Mult., pp. 2292-2295, 2019.
19 R. Raguram, J. M. Frahm, M. Pollefeys, "A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus", In Proc. European Conf. computer vision, pp. 500-513, 2008.