Deutsch
Bibtex
Cite as text
@Select Types{,
Journal = "Band-1",
Title= "Review and Systematization of Solutions for 3D Object Detection",
Author= "Jonas Friederich, Patrick Zschech",
Doi= "https://doi.org/10.30844/wi_2020_r2-friedrich",
Abstract= "Since 2017 there has been an exponential growth in scientific publications regarding the field of 3D object detection (3DOD). On the one hand, this growth can be explained by the strong demand for autonomous vehicles, and on the other hand, by the wide availability of 3D sensors. Due to the strong heterogeneity of developed approaches, this paper aims to identify, analyze and systematize publications that propose end-to-end solutions for 3DOD towards the goal to provide a structured framework which can guide future development, evaluation and application activities. To carry out the research, a systematic literature review is combined with a taxonomy development approach. The resulting framework consists of six dimensions, covering the addressed domains, applied datasets, sensor properties, data representation formats, modeling techniques, and evaluation criteria. The taxonomy can help researchers and practitioners to get a quick overview about the field by decomposing 3DOD solutions into more manageable pieces.
",
Keywords= "Data Science, Computer Vision, 3D Object Detection, Taxonomy.
",
}
Jonas Friederich, Patrick Zschech: Review and Systematization of Solutions for 3D Object Detection. Online: https://doi.org/10.30844/wi_2020_r2-friedrich (Abgerufen 25.12.24)
Open Access
Abstract
Abstract
Since 2017 there has been an exponential growth in scientific publications regarding the field of 3D object detection (3DOD). On the one hand, this growth can be explained by the strong demand for autonomous vehicles, and on the other hand, by the wide availability of 3D sensors. Due to the strong heterogeneity of developed approaches, this paper aims to identify, analyze and systematize publications that propose end-to-end solutions for 3DOD towards the goal to provide a structured framework which can guide future development, evaluation and application activities. To carry out the research, a systematic literature review is combined with a taxonomy development approach. The resulting framework consists of six dimensions, covering the addressed domains, applied datasets, sensor properties, data representation formats, modeling techniques, and evaluation criteria. The taxonomy can help researchers and practitioners to get a quick overview about the field by decomposing 3DOD solutions into more manageable pieces.
Keywords
Schlüsselwörter
Data Science, Computer Vision, 3D Object Detection, Taxonomy.
References
Referenzen
1. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: Unified, Real- Time Object Detection. In: arXiv:1506.02640 [cs]. pp. 779–788 (2016).
2. Zhao, Z.-Q., Zheng, P., Xu, S.-T., Wu, X.: Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems. 1–21 (2019).
3. Heinrich, K., Zschech, P., Möller, B., Breithaupt, L., Maresch, J.: Objekterkennung im Weinanbau – Eine Fallstudie zur Unterstützung von Winzertätigkeiten mithilfe von Deep Learning. HMD Praxis der Wirtschaftsinformatik. 56, 964–985 (2019).
4. Heinrich, K., Roth, A., Zschech, P.: Everything Counts: A Taxonomy of Deep Learning Approaches for Object Counting. In: European Conference on Information Systems. Stockholm-Uppsala, Sweden (2019).
5. Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum PointNets for 3D Object Detection from RGB-D Data. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 918–927 (2018).
6. Arnold, E., Al-Jarrah, O.Y., Dianati, M., Fallah, S., Oxtoby, D., Mouzakitis, A.: A Survey on 3D Object Detection Methods for Autonomous Driving Applications. IEEE Transactions on Intelligent Transportation Systems. 1–14 (2019).
7. vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., Cleven, A.: Reconstructing the Giant: On the Importance of Rigour in Documenting The Literature Search Process. In: European Conference on Information Systems. Verona, Italy (2009).
8. Nickerson, R.C., Varshney, U., Muntermann, J.: A method for taxonomy development and its application in information systems. European Journal of Information Systems. 22, 336– 359 (2013).
9. Szeliski, R.: Computer Vision: Algorithms and Applications. Springer Science & Business Media (2010).
10. Heinrich, K., Zschech, P., Skouti, T., Griebenow, J., Riechert, S.: Demystifying the Black Box: A Classification Scheme for Interpretation and Visualization of Deep Intelligent Systems. In: Americas Conference on Information Systems. Cancún, Mexico (2019).
11. Davies, E.R.: Computer and Machine Vision: Theory, Algorithms, Practicalities. Elsevier, Amsterdam; Boston (2012).
12. Microsoft: Kinect, https://developer.microsoft.com/en-us/windows/kinect, last accessed 2019/10/10.
13. Velodyne: HDL-64E, https://velodynelidar.com/hdl-64e.html, last accessed 2019/10/10.
14. Otepka, J., Ghuffar, S., Waldhauser, C., Hochreiter, R., Pfeifer, N.: Georeferenced Point Clouds: A Survey of Features and Point Cloud Management. ISPRS Int. J. Geo- Information. 2, 1038–1065 (2013).
15. Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D Object Detection Network for Autonomous Driving. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6526–6534 (2017).
16. Kitchenham, B., Charters, S.: Guidelines for performing Systematic Literature Reviews in Software Engineering. (2007).
17. Gregor, S.: The Nature of Theory in Information Systems. MIS Quarterly. 30, 611–642 (2006).
18. Zschech, P., Bernien, J., Heinrich, K.: Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation. In: International Conference on Information Systems. Munich, Germany (2019).
19. Zschech, P.: A Taxonomy of Recurring Data Analysis Problems in Maintenance Analytics. In: European Conference on Information Systems. Portsmouth, UK (2018).
20. Kurgan, L.A., Musilek, P.: A Survey of Knowledge Discovery and Data Mining Process Models. The Knowledge Engineering Review. 21, 1–24 (2006).
21. Lin, D., Fidler, S., Urtasun, R.: Holistic Scene Understanding for 3D Object Detection with RGBD Cameras. In: 2013 IEEE International Conference on Computer Vision. pp. 1417–1424 (2013).
22. Huang, S., Qi, S., Xiao, Y., Zhu, Y., Wu, Y.N., Zhu, S.-C.: Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation. In: International Conference on Neural Information Processing Systems. pp. 206–217 (2018).
23. Ren, Z., Sudderth, E.B.: Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts. arXiv:1906.04725 [cs]. 1–14 (2019).
24. Beltrán, J., Guindel, C., Moreno, F.M., Cruzado, D., García, F., Escalera, A.D.L.: BirdNet: A 3D Object Detection Framework from LiDAR Information. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). pp. 3517–3523 (2018).
25. Wang, Z., Zhan, W., Tomizuka, M.: Fusing Bird’s Eye View LIDAR Point Cloud and Front View Camera Image for 3D Object Detection. In: 2018 IEEE Intelligent Vehicles Symposium (IV). pp. 1–6 (2018).
26. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor Segmentation and Support Inference from RGBD Images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., and Schmid, C. (eds.) Computer Vision – ECCV 2012. pp. 746–760. Springer Berlin Heidelberg (2012).
27. Song, S., Lichtenberg, S.P., Xiao, J.: SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 567–576. IEEE, Boston, MA, USA (2015).
28. Hinterstoisser, S., Holzer, S., Cagniart, C., Ilic, S., Konolige, K., Navab, N., Lepetit, V.: Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: 2011 International Conference on Computer Vision. pp. 858–865. IEEE, Barcelona, Spain (2011).
29. Geiger, A., Lenz, P., Urtasun, R.: Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3354–3361 (2012).
30. Weng, X., Kitani, K.: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 [cs]. (2019).
31. Jörgensen, E., Zach, C., Kahl, F.: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. arXiv:1906.08070 [cs]. 1–10 (2019).
32. Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., Urtasun, R.: Monocular 3D Object Detection for Autonomous Driving. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2147–2156 (2016).
33. Mousavian, A., Anguelov, D., Flynn, J., Košecká, J.: 3D Bounding Box Estimation Using Deep Learning and Geometry. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5632–5640 (2017).
34. Zakharov, S., Shugurov, I., Ilic, S.: DPOD: 6D Pose Object Detector and Refiner. arXiv:1902.11020 [cs]. (2019).
35. Qin, Z., Wang, J., Lu, Y.: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. arXiv:1906.01193 [cs]. 1–19 (2019).
36. Sun, H., Meng, Z., Du, X., Ang, M.H.: A 3D Convolutional Neural Network Towards Real-Time Amodal 3D Object Detection. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 8331–8338 (2018).
37. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In: arXiv:1612.00593 [cs]. pp. 652–660 (2017).
38. Qi, C.R., Litany, O., He, K., Guibas, L.J.: Deep Hough Voting for 3D Object Detection in Point Clouds. arXiv:1904.09664 [cs]. (2019).
39. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision. 88, 303–338 (2010).
40. Heinrich, K., Janiesch, C., Möller, B., Zschech, P.: Is Bigger Always Better? Lessons Learnt from the Evolution of Deep Learning Architectures for Image Classification. In: Pre-ICIS SIGDSA Symposium. Munich, Germany (2019).
41. Maisano, R., Tomaselli, V., Capra, A., Longo, F., Puliafito, A.: Reducing Complexity of 3D Indoor Object Detection. In: 2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI). pp. 1–6 (2018).
Als PDF herunterladen