• Acta Optica Sinica
  • Vol. 38, Issue 11, 1110001 (2018)
Xiaohui Wang1、2、*, Lushen Wu1、*, Huawei Chen1, Yun Hu1, and Yaying Shi1
Author Affiliations
  • 1 School of Mechatronic Engineering, Nanchang University, Nanchang, Jiangxi 330031, China
  • 2 School of Architectural and Mechanical Engineering, Chifeng University, Chifeng, Inner Mongolia 0 24000, China
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    DOI: 10.3788/AOS201838.1110001 Cite this Article Set citation alerts
    Xiaohui Wang, Lushen Wu, Huawei Chen, Yun Hu, Yaying Shi. Feature Line Extraction from a Point Cloud Based on Region Clustering Segmentation[J]. Acta Optica Sinica, 2018, 38(11): 1110001 Copy Citation Text show less
    Overall procedure of the proposed method. (a) Original point cloud; (b) region clustering segmentation result; (c) feature point recognition result; (d) generation of the feature lines
    Fig. 1. Overall procedure of the proposed method. (a) Original point cloud; (b) region clustering segmentation result; (c) feature point recognition result; (d) generation of the feature lines
    Flow chart of the feature point recognition stage
    Fig. 2. Flow chart of the feature point recognition stage
    Feature line extraction from Model 1. (a) Original point cloud of Model 1; (b) region clustering segmentation; (c) set of candidate feature points; (d) set of feature points; (e) feature line extraction results
    Fig. 3. Feature line extraction from Model 1. (a) Original point cloud of Model 1; (b) region clustering segmentation; (c) set of candidate feature points; (d) set of feature points; (e) feature line extraction results
    Feature line extraction from Model 2. (a) Triangulated lighting model of Model 2; (b) region clustering segmentation; (c) set of candidate feature points; (d) set of feature points; (e) feature line extraction results
    Fig. 4. Feature line extraction from Model 2. (a) Triangulated lighting model of Model 2; (b) region clustering segmentation; (c) set of candidate feature points; (d) set of feature points; (e) feature line extraction results
    Feature line extraction from Model 3. (a) Triangulated lighting model of Model 3; (b) region clustering segmentation; (c) set of candidate feature points; (d) set of feature points; (e) feature line extraction results
    Fig. 5. Feature line extraction from Model 3. (a) Triangulated lighting model of Model 3; (b) region clustering segmentation; (c) set of candidate feature points; (d) set of feature points; (e) feature line extraction results
    Feature line extraction from Model 4. (a) Triangulated lighting model of Model 4; (b) region clustering segmentation; (c) set of candidate feature points; (d) set of feature points; (e) feature line extraction results
    Fig. 6. Feature line extraction from Model 4. (a) Triangulated lighting model of Model 4; (b) region clustering segmentation; (c) set of candidate feature points; (d) set of feature points; (e) feature line extraction results
    Comparison of the feature extraction methods used in Model 4. (a) Ref. [13] method; (b) proposed method
    Fig. 7. Comparison of the feature extraction methods used in Model 4. (a) Ref. [13] method; (b) proposed method
    Comparison of the feature extraction methods used in Model 5. (a) Triangulated lighting model of Model 5; (b) Ref. [13] method; (c) proposed method
    Fig. 8. Comparison of the feature extraction methods used in Model 5. (a) Triangulated lighting model of Model 5; (b) Ref. [13] method; (c) proposed method
    Feature line extracted by the proposed method using different neighborhood scales. (a1) Original point cloud and its partial enlarged detail in Model 3; extraction effects with (a2) k=10, (a3)k=16, (a4)k=25; (a5) feature line of Model 3; (b1) original point cloud and its partial enlarged detail in Model 4; extraction effects with (b2) k=10, (b3) k=16, (b4) k=25; (b5) feature line of Model 4
    Fig. 9. Feature line extracted by the proposed method using different neighborhood scales. (a1) Original point cloud and its partial enlarged detail in Model 3; extraction effects with (a2) k=10, (a3)k=16, (a4)k=25; (a5) feature line of Model 3; (b1) original point cloud and its partial enlarged detail in Model 4; extraction effects with (b2) k=10, (b3) k=16, (b4) k=25; (b5) feature line of Model 4
    Results of the proposed method for noisy datasets. (a)-(c) Add noise of 15 dB, 20 dB and 30 dB in Model 2, respectively; (d)-(f) add noise of 45 dB, 50 dB and 60 dB in Model 4, respectively
    Fig. 10. Results of the proposed method for noisy datasets. (a)-(c) Add noise of 15 dB, 20 dB and 30 dB in Model 2, respectively; (d)-(f) add noise of 45 dB, 50 dB and 60 dB in Model 4, respectively
    ModelNumber of original point cloudsNumber of candidate feature pointsNumber of feature points
    Model 161771729803
    Model 21329429241728
    Model 34000087995199
    Model 454465130717080
    Model 52590159573626
    Table 1. Complexity in different stages
    ModelCandidate feature point extraction time /sFeature point extraction time /sFeature line generation time /s
    Model 10.0400.0390.241
    Model 20.0900.0990.379
    Model 30.3460.2861.763
    Model 40.5010.4121.837
    Model 50.1900.1640.796
    Table 2. Duration of the feature extraction pipeline in seconds
    Xiaohui Wang, Lushen Wu, Huawei Chen, Yun Hu, Yaying Shi. Feature Line Extraction from a Point Cloud Based on Region Clustering Segmentation[J]. Acta Optica Sinica, 2018, 38(11): 1110001
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