Shah Rajvi Ajaybahai  received her doctorate in Computer Science and Engineering. Her research work was supervised by Prof. P J Narayanan . Here’s a summary of  Shah Rajvi Ajaybahai’s thesis,  Geometry-aware methods for efficient and accurate 3D reconstruction as explained by her:  

Advancements in 3D sensing and reconstruction have made a huge leap for modeling large-scale environments from monocular images using structure from motion (SfM) and simultaneous localization and mapping (SLAM) algorithms. SfM and SLAM based 3D reconstruction has applications for digital archival and modeling of real-world objects and environments, visual localization for geo-tagging and information retrieval, and mapping and navigation for robotic and autonomous driving applications.

In this thesis, we address problems in the area of large-scale structure from motion (SfM) for 3D reconstruction and localization. We introduce new methods for improving efficiency and accuracy of state-of-the-art pipeline for structure from motion. Large-scale SfM pipeline deals with large unorganized collections of images pertaining to a particular geographical site. These image collections are formed by either retrieving relevant images using textual queries from the Internet, or can be captured for the specific purpose of 3D modeling, mapping, and navigation. Internet image collections tend to be more noisy and present more challenges for reconstruction as compared to datasets captured with specific intention to reconstruct. In this thesis, we propose methods that help with organizing these large, unstructured, and noisy images into a structure that is useful for SfM methods, a match-graph (or a view-graph). We first propose a geometry-aware two stage approach for pairwise image matching that is both more efficient and superior in quality of correspondences. We then extend this idea to SfM pipeline and present an iterative multistage framework for coarse to fine 3D reconstruction. Finally, we suggest that a key to solving many of the reconstruction problems is to address the problem of filtering and improving the view-graph in a way that is specific to the underlying problem. To this effect, we propose a unified framework for view-graph selection and show its application to achieve multiple reconstruction objectives.