Fig. 1. Geometric indication of multi-view triangulation.

Fig. 2. Four types of synthetic triangulation instances. (a) Type A: cameras and 3D points are randomly distributed; (b) Type B: the camera moves along a curved trajectory around 3D points; (c) Type C: the camera moves on a circle while 3D points are located at the center; (d) Type D: the camera moves along a curved trajectory towards the 3D scene.

Fig. 3. Time and accuracy analysis with different noise levels on Type D synthetic data. (a), (b), and (c) are the results of the time, 3D error, and 2D error when the Gaussian covariance is set as 2 pixels; (d), (e), and (f) are the results of the time, 3D error, and 2D error when the Gaussian covariance is set as 5 pixels; (g), (h), and (i) are the results of the time, 3D error, and 2D error when the Gaussian covariance is set as 8 pixels.

Fig. 4. Overall convergence of INT with different datasets. (a) Convergence curve of Type A dataset; (b) convergence curve of Type B dataset; (c) convergence curve of Type C dataset; (d) convergence curve of Type D dataset.

Fig. 5. INT based on real datasets. (a) Lund Cathedral, (b) Orebro Castle, (c) Ystad Monestary, and (d) Skansen Kronan.

Table 1. Comparisons between Different Incremental Triangulation Performances

Table 2. Runtime Comparisons of Different Methods with Real Datasets^a