Fig. 1. Comparison between Euclidean distance and geodesic distance. (a) Euclidean distance; (b) geodesic distance

Fig. 2. Calculation method of geodesic distance. (a) Directed weighted graph in space; (b) weight between two points; (c) shortest path between two points

Fig. 3. Flowchart of the thermal gradient method

Fig. 4. Selection of neighbourhood feature points. (a) Curve with little surface change; (b) curve with large surface changes

Fig. 5. Flowchart of fine registration

Fig. 6. Cross-section of multiview point cloud registration. (a) Multiview point cloud registration model; (b) the initial cross-section of fine registration; (c) the results of the MAICP method; (d) the results of the LRS method; (e) the results of the JRMPC method; (f) the results of K-means method; (g) the results of the proposed method

Fig. 7. Comparison of the local effect of cross-section. (a) The local magnification effect of the registration result of the Dragon model obtained by K-means method; (b) the local magnification effect of the registration result of the Dragon model obtained by the proposed method; (c) the local magnification effect of the registration result of the Chicken model obtained by JRMPC method; (d) the local magnification effect of the registration result of the Chicken model obtained by the proposed method

Table 1. Point cloud data information

Table 2. Accuracy comparison of different methods

Table 3. Efficiency comparison of two kinds of geodesic distance matrix calculation methods

Table 4. Efficiency comparison of different registration methods

Table 5. Robustness comparison of different methods for the Bunny dataset

Table 6. Robustness comparison of different methods for the Dragon dataset

Table 7. Robustness comparison of different methods for the Happy dataset

Table 8. Robustness comparison of different methods for the Chicken dataset