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Articles

The applications of robust estimation method BaySAC in indoor point cloud processing

Pages 182-187 | Received 15 Mar 2016, Accepted 18 Jun 2016, Published online: 08 Oct 2016

Figures & data

Figure 1. 2D hypothesis model parameters histogram of a plane.

Figure 1. 2D hypothesis model parameters histogram of a plane.

Figure 2. Flowchart of BaySAC-CONV algorithm.

Figure 2. Flowchart of BaySAC-CONV algorithm.

Figure 3. Identified 3D correspondences: (a) Correspondences in scan A; (b) Correspondences in scan B.

Figure 3. Identified 3D correspondences: (a) Correspondences in scan A; (b) Correspondences in scan B.

Figure 4. Illustration of check points.

Figure 4. Illustration of check points.

Table 1. Evaluation of registration accuracy in terms of average point-to-point distance.

Table 2. Computational efficiencies of the proposed strategies (Iteration).

Table 3. Computational efficiencies of the proposed strategies (unit: ms).

Figure 5. Comparison of the fitting results of different methods on Data-set II: (a) RANSAC; (b) BaySAC-CONV.

Figure 5. Comparison of the fitting results of different methods on Data-set II: (a) RANSAC; (b) BaySAC-CONV.

Figure 6. Comparison of the fitting results of different methods before and after the point cloud simplification of Data-set III: (a) RANSAC; (b) BaySAC-CONV.

Figure 6. Comparison of the fitting results of different methods before and after the point cloud simplification of Data-set III: (a) RANSAC; (b) BaySAC-CONV.

Table 4. Comparison of fitting results.