A new color distance measure formulated from the cooperation of the Euclidean and the vector angular differences for lidar point cloud segmentation

Abstract

Two important features of the points in the LiDAR point clouds are the spatial and the color features. The spatial feature is mostly used in the point cloud processing field due to its 3D informative and distinctive characteristic. The local geometric difference derived from the spatial features of the points is usually benefited by graph-based point cloud segmentation methods, because the geometric features of the local point groups are highly distinctive. In this paper, we use both the geometric and color differences of the adjacent local point groups at the impact rates 0.3, 0.5, and 0.7 and cooperate the Euclidean and the vector color differences within several averaging techniques for the color difference. The difference forms have been tested within a graph-based segmentation method on four point cloud segmentation datasets, two indoor and two outdoor, using their spatial and color information. The geometric mean as an averaging techniques increases the segmentation success for the all datasets except one outdoor when the color differences are used in the segmentation at the impact rate 0.3, while the harmonic mean increases the success for the all datasets the successes except the other outdoor at the same impact rate. According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.

Keywords

• Point Cloud Segmentation
• Weighting
• Color Distance
• Measurement Vector
• Angular Difference

References

1. Aijazi A K, Checchin P & Trassoudaine L (2013). Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation. Remote Sensing, 5(4), 1624–1650. https://doi.org/10.3390/rs5041624
2. Armeni I, Sener O, Zamir A R, Jiang H, Brilakis I, Fischer M & Savarese S (2016). 3D semantic parsing of large-scale indoor spaces. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2016.170
3. Awrangjeb M & Fraser C S (2014). An automatic and threshold-free performance evaluation system for building extraction techniques from airborne LIDAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(10), 4184–4198. https://doi.org/10.1109/JSTARS.2014.2318694
4. Barnea S & Filin S (2013). Segmentation of terrestrial laser scanning data using geometry and image information. ISPRS Journal of Photogrammetry and Remote Sensing, 76, 33–48. https://doi.org/10.1016/j.isprsjprs.2012.05.001
5. Bassier M, Bonduel M, Van Genechten B & Vergauwen M (2017). Segmentation of large unstructured point clouds using octree-based region growing and conditional random fields. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives XLII-2/W8, 25–30. https://doi.org/10.5194/isprs-archives-XLII-2-W8-25-2017
6. Chen B, Shi S., Sun J, Gong W, Yang J, Du L, Guo K, Wang, B, Chen, B (2019). Hyperspectral lidar point cloud segmentation based on geometric and spectral information. Optics Express, 27(17). https://doi.org/10.1364/oe.27.024043
7. Dutta A, Engels J & Hahn M (2014). A distance-weighted graph-cut method for the segmentation of laser point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40(3), 81–88. https://doi.org/10.5194/isprsarchives-XL-3-81-2014
8. Felzenszwalb P F & Huttenlocher D P (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167–181. https://doi.org/10.1023/B:VISI.0000022288.19776.77

9. Hackel T, Savinov N, Ladicky L, Wegner J D, Schindler K & Pollefeys M (2017). Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. https://doi.org/10.5194/isprs-annals-IV-1-W1-91-2017
10. Lari Z & Habib A (2014). An adaptive approach for the segmentation and extraction of planar and linear/cylindrical features from laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 192–212. https://doi.org/10.1016/j.isprsjprs.2013.12.001
11. Li L, Yang F, Zhu H, Li D, Li Y & Tang L (2017). An improved RANSAC for 3D point cloud plane segmentation based on normal distribution transformation cells. Remote Sensing 9(5). https://doi.org/10.3390/rs9050433
12. Lohmann G (1998). Volumetric image analysis. Wiley.
13. Papon J, Abramov A, Schoeler M & Worgotter F (2013). Voxel cloud connectivity segmentation - Supervoxels for point clouds. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2027–2034. https://doi.org/10.1109/CVPR.2013.264
14. Polak M, Zhang H & Pi M (2009). An evaluation metric for image segmentation of multiple objects. Image and Vision Computing, 27(8), 1223-1227. https://doi.org/10.1016/j.imavis.2008.09.008
15. Rabbani T, van den Heuvel F A & Vosselman G (2006). Segmentation of point clouds using smoothness constraint. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences - Commission V Symposium “Image Engineering and Vision Metrology” 36, 248–253. https://doi.org/10.1111/1750-3841.12802
16. Saglam A & Baykan N A (2019). Evaluating the attributes of remote sensing image pixels for fast k-means clustering. Turkish Journal of Electrical Engineering & Computer Sciences, 27, 4188–4202. https://doi.org/10.3906/elk-1901-190
17. Stein S C, Schoeler M, Papon J & Worgotter F (2014). Object partitioning using local convexity. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 304–311. https://doi.org/10.1109/CVPR.2014.46
18. Strom J, Richardson A & Olson E (2010). Graph-based segmentation for colored 3D laser point clouds. IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, 2131–2136. https://doi.org/10.1109/IROS.2010.5650459
19. Su Y T, Bethel J & Hu S (2016). Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications. ISPRS Journal of Photogrammetry and Remote Sensing 113, 59–74. https://doi.org/10.1016/j.isprsjprs.2016.01.001
20. Verdoja F, Thomas D & Sugimoto A (2017). Fast 3D point cloud segmentation using supervoxels with geometry and color for 3D scene understanding. Proceedings - IEEE International Conference on Multimedia and Expo, Hong Kong, China, 1285–1290. https://doi.org/10.1109/ICME.2017.8019382
21. Vo A V, Truong-Hong L, Laefer D F & Bertolotto M (2015). Octree-based region growing for point cloud segmentation. ISPRS Journal of Photogrammetry and Remote Sensing 104, 88–100. https://doi.org/10.1016/j.isprsjprs.2015.01.011
22. Xu Y, Hoegner L, Tuttas S & Stilla U (2017). Voxel- and graph-based point cloud segmentation of 3D scenes using perceptual grouping laws. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1/W1, 43–50. https://doi.org/10.5194/isprs-annals-IV-1-W1-43-2017
23. Xu Y, Yao W, Tuttas S, Hoegner L & Stilla U (2018a). Unsupervised Segmentation of Point Clouds From Buildings Using Hierarchical Clustering Based on Gestalt Principles. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, 4270–4286. https://doi.org/10.1109/JSTARS.2018.2817227
24. Xu Y, Yao W, Tuttas S, Hoegner L & Stilla U (2018b). Building-Segmentation-Reference-Dataset [WWW Document]. URL https://github.com/Yusheng-Xu/Building-Segmentation-Reference-Dataset
25. Zhu Q, Li Y, Hu H & Wu B (2017). Robust point cloud classification based on multi-level semantic relationships for urban scenes. ISPRS Journal of Photogrammetry and Remote Sensing, 129, 86–102. https://doi.org/10.1016/j.isprsjprs.2017.04.022