Fig 1.
Two examples of laser scanning scene.
A and B are respectively the artwork image and its point cloud in [6]. C and D display the indoor scene image and its ccorresponding point cloud data.
Fig 2.
Overview of our method.
Fig 3.
Different outlier distribution.
Fig 4.
Different δ in our method.
Fig 5.
Local fitting plane and projection of point.
Fig 6.
A: pending data. B: k = 4. C: k = 10. D: k = 50.
Fig 7.
3D models from Princeton shape Database.
A: Original model. B: point cloud model with outliers.
Fig 8.
Outlier removal for table model.
A: Table model with different outliers, B: isolated outlier removal, C: sparse outlier are removal, D: non-isolated outlier removal result.
Fig 9.
Outlier removal for chair model.
A: Chair model with different outliers, B: isolated outlier removal, C: sparse outlier removal, D: non-isolated outlier removal result.
Fig 10.
Outlier removal for bird model.
A: Bird model with different outliers, B: isolated outlier removal, C: sparse outlier removal, D: non-isolated outlier removal result.
Fig 11.
Outlier removal for bear model.
A: Bear model with different outliers, B: isolated outlier is removed, C: sparse outlier is removed, D: non-isolated outlier removal result.
Fig 12.
Outlier removal for monster model.
A: Monster model with different outliers, B: isolated outlier is removed, C: sparse outlier is removed, D: non-isolated outlier removal result.
Fig 13.
Outlier removal for Nail model.
A: Nail model with different outliers, B: isolated outlier is removed, C: sparse outlier is removed, D: non-isolated outlier removal result.
Fig 14.
Outlier removal for indoor scene S1.
A: Original Indoor scene S1, B: Outlier removal result.
Fig 15.
Outlier removal for indoor scene S2.
A: Original Indoor scene S2, B: Outlier removal result.
Fig 16.
Comparison result for table data between our outlier removal method and the classic two method.
A: Our methods, B: Statistics-based method; C: Radius based method.
Fig 17.
Comparison result for chair data between our outlier removal method and the classic two method.
A: Our methods, B: Statistics-based method; C: Radius based method.
Fig 18.
Comparison result for bear data between our outlier removal method and the classic two method.
A: Our methods, B: Statistics-based method; C: Radius based method.
Fig 19.
Comparison result for bird data between our outlier removal method and the classic two method.
A: Our methods, B: Statistics-based method; C: Radius based method.
Fig 20.
Comparison result for monster data between our outlier removal method and the classic two method.
A: Our methods, B: Statistics-based method; C: Radius based method.
Fig 21.
Comparison result for Nail data between our outlier removal method and the classic two method.
A: Our methods, B: Statistics-based method; C: Radius based method.
Fig 22.
Comparison result for indoor scene S1 between our outlier removal method and the classic two method.
A: Statistics-based method; B: Radius based method; C: Our methods.
Fig 23.
Comparison result for indoor scene S2 between our outlier removal method and the classic two method.
A: Statistics-based method; B: Radius based method; C: Our methods.
Fig 24.
Running time comparison result for six 3D models.
Fig 25.
Running time comparison result for two indoor scene data.
Fig 26.
Comparison results on TORCH and DRAGON models.
A: Datasets, B: Point cloud; C: Our outlier removing method. D: Wolff et al. [6].
Fig 27.
Comparison results on STATUE model.
A: Datasets, B: Point cloud; C: Our outlier removing method. D: Wolff et al. [6].
Fig 28.
A: TORCH model, B: DRAGON model; C: STATUE model. (left: our method, right: method in [6]).
Table 1.
Timings and statistical data of point model by our method.
Table 2.
Performance analysis of our algorithm on 3D models.
Table 3.
Timings and parameter setting of 3D model.
Table 4.
Parameter setting in our method.