To enhance the proposed method for handling complex scenes like the Dr. Johnson scene, which presents global mapping challenges, several strategies can be implemented. First, integrating a global planner that utilizes a more comprehensive understanding of the scene's topology could significantly improve the accuracy of camera pose estimation and sparse point cloud generation. This planner could leverage techniques such as graph-based SLAM (Simultaneous Localization and Mapping) to maintain a global map that accounts for the spatial relationships between various features in the environment.
Additionally, incorporating multi-view geometry principles could help in refining the camera poses by utilizing more robust triangulation methods that consider the entire set of images rather than relying solely on local features. This would mitigate the issues arising from self-occlusions and complex geometries that often lead to inaccuracies in the sparse point cloud.
Furthermore, employing advanced SfM algorithms that are designed to work with limited input images could enhance the initialization phase. Techniques such as bundle adjustment could be utilized to optimize the camera poses and 3D points collectively, ensuring that the reconstruction is more resilient to noise and inaccuracies in the initial data.
Lastly, integrating machine learning approaches that predict the best camera poses based on learned features from previous reconstructions could provide a more adaptive solution, allowing the system to better navigate and reconstruct complex scenes.