激光与光电子学进展, 2019, 56 (5): 052804, 网络出版: 2019-07-31 复制并引用该论文  本文已被引 12 次  被引通知

图 & 表

图 1. 多尺度特征与PointNet结合的深度神经网络模型

Fig. 1. Deep neural network model combining multiscale features with PointNet

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图 2. PointNet网络架构

Fig. 2. PointNet network architecture

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图 3. 点云中不同尺度下的邻域。(a)尺度一;(b)尺度二;(c)尺度三

Fig. 3. Neighbors of different scales in point clouds. (a) Scale 1; (b) scale 2; (c) scale 3

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图 4. Semantic 3D数据集的点云。(a)区域1;(b)区域2

Fig. 4. Point cloud of Semantic 3D dataset. (a) Area 1; (b) area 2

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图 5. Vaihingen城市数据集的点云。(a)区域1;(b)区域2;(c)区域3

Fig. 5. Point cloud of Vaihingen city dataset. (a) Area 1; (b) area 2; (c) area 3

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图 6. Semantic 3D数据集的分类结果。(a)输入点云;(b) PointNet;(c)所提算法

Fig. 6. Classification results of Semantic 3D dataset. (a) Input point cloud; (b) PointNet; (c) proposed algorithm

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图 7. Vaihingen城市数据集的分类结果。(a)输入点云;(b) PointNet;(c)所提算法

Fig. 7. Classification results of Vaihingen city dataset. (a) Input point cloud; (b) PointNet; (c) proposed algorithm

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表 1不同尺度下的实验结果

Table1. Experimental results of different scales

Scale s=2 s=3 s=4 Multi-scale 90.7 89.8 89.2 Scale 1 85.2 85.2 85.2 Scale 2 86.4 87.3 86.1 Scale 3 86.1 84.9 84.3

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表 2Semantic 3D数据集的各类别IoU

Table2. Each category IoU of Semantic 3D dataset%

Algorithm MeanIoU Man-madeterrain Naturalterrain Highvegetation Lowvegetation Buildings Hardscape Scanningartefacts Cars Ref. [22] 58.5 85.6 83.2 74.2 32.4 89.7 18.5 25.1 59.2 Ref. [23] 59.1 82.0 77.3 79.7 22.9 91.1 18.4 37.3 64.4 Ref. [24] 61.3 83.9 66.0 86.0 40.5 91.1 30.9 27.5 64.3 Proposed 67.4 85.6 87.1 90.5 42.3 93.2 31.6 40.8 67.8

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表 3Semantic 3D数据集的分类精度以及运行时间

Table3. Classification accuracy and runtime of Semantic 3D dataset

Algorithm MeanIoU /% Overallaccuracy /% Runtime /s Ref. [22] 58.5 88.9 - Ref. [23] 59.1 88.6 3600.00 Ref. [24] 61.3 88.1 1881.00 Proposed 67.4 90.7 4300.00

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表 4Vaihingen城市数据集的各类别IoU

Table4. Each category IoU of Vaihingen city dataset%

Algorithm MeanIoU /% Power line Car Lowvegetation Impervioussurfaces Roof Fence /hedge Facade Shrub Tree PointNet 32.0 0.8 23.2 32.1 47.6 84.7 2.3 5.7 15.4 76.2 Proposed 34.9 1.2 34.3 36.9 49.3 86.8 2.6 4.8 13.3 85.7

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表 5Vaihingen城市数据集的分类精度以及运行时间

Table5. Classification accuracy and runtime of Vaihingen city dataset

Algorithm Mean IoU /% Overall accuracy /% Average class accuracy /% Runtime /s PointNet 32.0 65.2 38.1 1500.00 Proposed 34.9 74.3 43.6 2300.00

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赵中阳, 程英蕾, 释小松, 秦先祥, 李鑫. 基于多尺度特征和PointNet的LiDAR点云地物分类方法[J]. 激光与光电子学进展, 2019, 56(5): 052804. Zhongyang Zhao, Yinglei Cheng, Xiaosong Shi, Xianxiang Qin, Xin Li. Terrain Classification of LiDAR Point Cloud Based on Multi-Scale Features and PointNet[J]. Laser & Optoelectronics Progress, 2019, 56(5): 052804.