FAST SEMANTIC SEGMENTATION of 3D POINT CLOUDS with STRONGLY VARYING DENSITY

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 3, Issue , 2016, Pages 177-184

  Hackel, Timo ^a   Wegner, Jan D ^a   Schindler, Konrad ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

Features; LIDAR; Multiscale; Point Clouds; Scene Understanding; Semantic Classification

Indexed keywords

EID: 85046284404     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprs-annals-III-3-177-2016     Document Type: Conference Paper

References (46)

1. Belongie, S., Malik, J. and Puzicha, J., 2002. Shape matching and object recognition using shape contexts. IEEE TPAMI 24(4), pp. 509-522.
2. Blomley, R., Weinmann, M., Leitloff, J. and Jutzi, B., 2014. Shape distribution features for point cloud analysis-a geometric histogram approach on multiple scales. ISPRS Annals.
3. Boyko, A. and Funkhouser, T., 2011. Extracting roads from dense point clouds in large scale urban environment. ISPRS J Photogrammetry & Remote Sensing 66(6), pp. 2-12.
4. Brodu, N. and Lague, D., 2012. 3d terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology. ISPRS J Photogrammetry & Remote Sensing 68, pp. 121-134.
5. Charaniya, A. P., Manduchi, R. and Lodha, S. K., 2004. Supervised parametric classification of aerial LiDAR data. CVPR Workshops.
6. Chehata, N., Guo, L. and Mallet, C., 2009. Airborne LiDAR feature selection for urban classification using random forests. ISPRS Archives.
7. Dalponte, M., Bruzzone, L. and Gianelle, D., 2012. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sensing of Environment 123, pp. 258-270.
8. Demantké, J., Mallet, C., David, N. and Vallet, B., 2011. Dimensionality based scale selection in 3d lidar point clouds. ISPRS Archives.
9. Dohan, D., Matejek, B. and Funkhouser, T., 2015. Learning Hierarchical Semantic Segmentation of LIDAR Data. International Conference on 3D Vision.
10. Elseberg, J., Borrmann, D. and Nüchter, A., 2013. One billion points in the cloud-an octree for efficient processing of 3d laser scans. ISPRS J Photogrammetry & Remote Sensing 76, pp. 76-88.
11. Friedman, J. H., Bentley, J. L. and Finkel, R. A., 1977. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software 3(3), pp. 209-226.
12. Frome, A., Huber, D., Kolluri, R., Bülow, T. and Malik, J., 2004. Recognizing objects in range data using regional point descriptors. ECCV.
13. Golovinskiy, A., Kim, V. G. and Funkhouser, T., 2009. Shapebased Recognition of 3D Point Clouds in Urban Environments. ICCV.
14. Haala, N., Brenner, C. and Anders, K.-H., 1998. 3d urban GIS from laser altimeter and 2d map data. ISPRS Archives.
15. Hackel, T., Wegner, J. D. and Schindler, K., 2016. Contour detection in unstructured 3d point clouds. CVPR.
16. He, K., Sun, J. and Tang, X., 2010. Guided image filtering. ECCV.
17. Hug, C. and Wehr, A., 1997. Detecting and identifying topographic objects in imaging laser altimetry data. ISPRS Archives.
18. Johnson, A. and Hebert, M., 1999. Using spin images for efficient object recognition in cluttered 3d scenes. IEEE TPAMI 21(5), pp. 433-449.
19. Kim, E. and Medioni, G., 2011. Urban scene understanding from aerial and ground LIDAR data. Machine Vision and Applications 22, pp. 691-703.
20. Lafarge, F. and Mallet, C., 2012. Creating large-scale city models from 3d-point clouds: a robust approach with hybrid representation. IJCV 99(1), pp. 69-85.
21. Lai, K., Bo, L. and Fox, D., 2014. Unsupervised feature learning for 3d scene labeling. ICRA.
22. Lalonde, J.-F., Vandapel, N., Huber, D. and Hebert, M., 2006. Natural terrain classification using three-dimensional ladar data for ground robot mobility. Journal of Field Robotics 23(1), pp. 839-861.
23. Li, Y., Wu, X., Chrysathou, Y., Sharf, A., Cohen-Or, D. and Mitra, N. J., 2011. Globfit: Consistently fitting primitives by discovering global relations. ACM Transactions on Graphics 30(4), pp. 52:1-52:12.
24. Maas, H.-G., 1999. The potential of height texture measures for the segmentation of airborne laserscanner data. International Airborne Remote Sensing Conference and Exhibition / Canadian Symposium on Remote Sensing.
25. Monnier, F., Vallet, B. and Soheilian, B., 2012. Trees detection from laser point clouds acquired in dense urban areas by a mobile mapping system. ISPRS Annals.
26. Muja, M. and Lowe, D. G., 2009. Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP.
27. Najafi, M., Namin, S. T., Salzmann, M. and Petersson, L., 2014. Non-associative Higher-Order Markov Networks for Point Cloud Classification. ECCV.
28. Niemeyer, J., Wegner, J. D., Mallet, C., Rottensteiner, F. and Soergel, U., 2011. Conditional Random Fields for Urban Scene Classification with Full Waveform LiDAR Data. Photogrammetric Image Analysis.
29. Orka, H. O., Naesset, E. and Bollandsas, O. M., 2012. Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sensing of Environment 123, pp. 258-270.
30. Pauly, M., Keiser, R. and Gross, M., 2003. Multi-scale feature extraction on point-sampled surfaces. Computer Graphics Forum 22(3), pp. 281-289.
31. Pu, S. and Vosselman, G., 2009. Knowledge based reconstruction of building models from terrestrial laser scanning data. ISPRS J Photogrammetry & Remote Sensing 64, pp. 575-584.
32. Pu, S., Rutzinger, M., Vosselman, G. and Oude Elberink, S., 2011. Recognizing basic structures from mobile laser scanning data for road inventory studies. ISPRS J Photogrammetry & Remote Sensing 66, pp. 28-39.
33. Rottensteiner, F. and Briese, C., 2002. A new method for building extraction in urban areas from high-resolution LIDAR data. ISPRS Archives.
34. Rusu, R., Holzbach, A., Blodow, N. and Beetz, M., 2009. Fast geometric point labeling using conditional random fields. IROS.
35. Schmidt, A., Niemeyer, J., Rottensteiner, F. and Soergel, U., 2014. Contextual Classification of Full Waveform Lidar Data in the Wadden Sea. IEEE Geoscience and Remote Sensing Letters 11(9), pp. 1614-1618.
36. Schnabel, R., Wahl, R. and Klein, R., 2007. Efficient RANSAC for point-cloud shape detection. Computer Graphics Forum 26(2), pp. 214-226.
37. Serna, A., Marcotegui, B., Goulette, F. and Deschaud, J.-E., 2014. Paris-rue-madame database: a 3d mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods. ICPRAM.
38. Shapovalov, R., Velizhev, A. and Barinova, O., 2010. Nonassociative markov networks for 3D point cloud classification. ISPRS Archives.
39. Tombari, F., Salti, S. and Di Stefano, L., 2010. Unique signatures of histograms for local surface description. ECCV.
40. Vallet, B., Brédif, M., Serna, A., Marcotegui, B. and Paparoditis, N., 2015. Terramobilita/iqmulus urban point cloud analysis benchmark. Computers & Graphics 49, pp. 126-133.
41. Vosselman, G., Gorte, B., Sithole, G. and Rabbani, T., 2004. Recognizing structure in laser scanner point clouds. ISPRS Archives.
42. Weinmann, M., Jutzi, B. and Mallet, C., 2013. Feature relevance assessment for the semantic interpretation of 3d point cloud data. ISPRS Annals.
43. Weinmann, M., Urban, S., Hinz, S., Jutzi, B. and Mallet, C., 2015. Distinctive 2d and 3d features for automated large-scale scene analysis in urban areas. Computers & Graphics 49, pp. 47-57.
44. Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X. and Xiao, J., 2015. 3D ShapeNets: A deep representation for volumetric shape modeling. CVPR.
45. Xiao, J. and Furukawa, Y., 2014. Reconstructing the world's museums. IJCV 110(3), pp. 243-258.
46. Yao, W., Hinz, S. and Stilla, U., 2011. Extraction and motion estimation of vehicles in single-pass airborne LiDAR data towards urban traffic analysis. ISPRS J Photogrammetry & Remote Sensing 66, pp. 260-271.