The last step is visualizing the triangulated 3D real-world points. An easy way of creating 3D scatterplots is by using matplotlib. However, if you are looking for more professional visualization tools, you may be interested in Mayavi (http://docs.enthought.com/mayavi/mayavi), VisPy (http://vispy.org), or the Point Cloud Library (http://pointclouds.org). Although the latter does not have Python support for point cloud visualization yet, it is an excellent tool for point cloud segmentation, filtering, and sample consensus model fitting. For more information, head over to strawlab's GitHub repository at https://github.com/strawlab/python-pcl.
Before we can plot our 3D point cloud, we obviously have to extract the [R | t] matrix and perform the triangulation as explained earlier:
def plot_point_cloud(self, feat_mode="SURF"):
self._extract_keypoints(feat_mode)
self._find_fundamental_matrix()
self._find_essential_matrix()
self._find_camera_matrices_rt...
