Fig. 1. Results of statistical filtering algorithm before and after processing. (a) Initial scanning point cloud; (b) point cloud after processing
Fig. 2. Results of different tensor voting methods. (a) Normal tensor voting method; (b) point tensor voting method
Fig. 3. Estimated results at different K values. (a) K=15; (b) K=5
Fig. 4. Filtering results of noise plane
Fig. 5. Point clouds of bird bottle model under different algorithms. (a) Initial point cloud; (b) proposed algorithm; (c) Ref. [4]; (d) Ref. [5]
Fig. 6. Point clouds of hand model under different algorithms. (a) Initial point cloud; (b) proposed algorithm; (c) Ref. [4]; (d) Ref. [5]
Fig. 7. Point clouds of buddha model under different algorithms. (a) Initial point cloud; (b) proposed algorithm; (c) Ref. [4]; (d) Ref. [5]
Fig. 8. Point clouds of stone model under different algorithms. (a) Initial point cloud; (b) proposed algorithm; (c) Ref. [4]; (d) Ref. [5]
Fig. 9. Point clouds of tortoise model under different algorithms.(a) Initial point cloud; (b) proposed algorithm; (c) Ref. [4]; (d) Ref. [5]
Model | Proposed algorithm | Ref. [4] | Ref. [5] |
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Bird bottle | 97.90 | 85.09 | 85.59 | Hand | 99.76 | 97.58 | 98.07 | Buddha | 98.99 | 92.80 | 96.01 | Stone | 99.87 | 93.72 | 95.87 | Tortoise | 99.23 | 89.90 | 93.31 |
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Table 1. Ratio of number of points between filter model and noiseless model unit: %
Model | Points | Running time of proposed algorithm/s | Running time of Ref. [4] /s | Running time of Ref. [5] /s |
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Bird bottle | 37778 | 31.3 | 16.6 | 29.3 | Hand | 50756 | 38.8 | 18.3 | 29.6 | Buddha | 29325 | 26.7 | 15.4 | 23.8 | Stone | 15406 | 20.3 | 13.2 | 18.9 | Tortoise | 22939 | 22.4 | 14.7 | 21.2 |
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Table 2. Points and processing time of different models