Chinese Journal of Lasers, Volume. 49, Issue 9, 0910003(2022)
Fusion Method of LiDAR Point Cloud and Dense Matching Point Cloud
Figures & Tables(9)
Fig. 2. Illustration of point cloud segmentation based on graph-cuts model. (a) Aligned result of two heterologous point clouds; (b) segmentation results
Fig. 3. Illustrations of guided point cloud filtering with blended point clouds. (a)(b) Terrestrial laser scanning (TLS) point cloud used only; (c)(d) proposed method
Fig. 4. TLS point cloud and dense matching point cloud of a laboratory building. (a) TLS point cloud; (b) dense matching point cloud; (c) aligned result of two different point clouds; (d) point cloud of area A1; (e) point cloud of area A2；(f) point cloud of area C; (g) point cloud of area B1; (h) point cloud of area B2; (i) point cloud of area D
Fig. 5. Fusing results of two cross-source point clouds by using proposed method. (a) Dense matching point cloud processed by using proposed segmentation method; (b) dense matching point cloud segmented and smoothed by using proposed method; (c) segmented dense matching point cloud and TLS point cloud; (d) final dense matching point cloud and TLS point cloud processed by proposed method
Fig. 6. Comparison between facade LiDAR point cloud and dense matching point cloud of a laboratory building. (a) Aligned result of LiDAR point cloud and dense matching point cloud; (b) blended point clouds after segmentation; (c) comparison between original dense matching point cloud and smoothed point cloud based on progressive migration method; (d) comparison between original dense matching point cloud and smoothed point cloud based on proposed method; (e) smoothed dense matching point cloud based on progressive migration method; (f) smoothed dense matching point cloud based on proposed method; (g) blended point clouds with overlapped area; (h) comparison between original dense matching point cloud and smoothed point cloud based on progressive migration method; (i) comparison between original dense matching point cloud and smoothed point cloud based on proposed method; (j) comparison between two smoothed dense matching point clouds based on proposed method and progressive migration method
Table 1. Detailed description of three datasets
View tableTable 1. Detailed description of three datasets
Dataset Types of point cloud Quantity of point cloud /106 Quantity of TLS stations Quantity of photos Method of photo-graphing Method of MVS reconstruction Distance between the points /m Laboratory building TLS 35.45 8 0.01 Dense matching 31.64 78 Aerial oblique photography Smart 3D 0.017 Courthouse TLS 59.14 19 0.006 Dense matching 27.05 1106 Groud-view COLMAP 0.013 Barn TLS 12.74 9 0.004 Dense matching 5.42 179 Groud-view COLMAP 0.02
Table 2. Parameters used in experiment
View tableTable 2. Parameters used in experiment
Parameter Value Point neighbourhood for normals and KNN graph 10 Blending distance control /m 0.1 Blending colour control 6 Blending smoothness 2 Point neighbourhood for guided point cloud filter 50 Filtering quality control 0.1 Radius parameter for filtering /m 0.3
Table 3. Quantitative evaluation of smoothing effect of dense matching point cloud on TanksandTemples benchmark at evaluation threshold of 10 cm
View tableTable 3. Quantitative evaluation of smoothing effect of dense matching point cloud on TanksandTemples benchmark at evaluation threshold of 10 cm
Method Evaluation for Courthouse Evaluation for Barn Accuracy /% Completeness /% Time /s Accuracy /% Completeness /% Time /s COLMAP 67.41 75.37 45.99 97.65 Ours without guided filter 65.79 75.01 41.29 88.03 Method in Ref.[ 6 ]67.51 77.96 171 45.67 89.59 80 Method in Ref.[ 17 ]70.11 79.82 817 53.54 90.96 173 Ours 71.17 80.25 992 53.66 91.02 195
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Li Yan, Dawei Ren, Hong Xie, Pengcheng Wei. Fusion Method of LiDAR Point Cloud and Dense Matching Point Cloud[J]. Chinese Journal of Lasers, 2022, 49(9): 0910003
Paper Information
Received: Sep. 17, 2021
Accepted: Oct. 25, 2021
Published Online: Apr. 22, 2022
The Author Email: Hong Xie (hxie@sgg.whu.edu.cn)
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