Multi-view Homographies from Dense Matches - Nilsson Gisleskog,Gustav (Master’s Theses in Mathematical Sciences, 2025)
We propose a SLAM system that reconstructs 3D scenes by leveraging dense feature correspondences and planar structures, identified through multi-view homographies. In contrast to traditional SLAM algorithms, our method emphasizes planes as the primary geometric primitive. While classical keypoint detectors such as SIFT typically yield a few hundred matches per image pair, modern dense matchers can produce on the order of 10,000 matches. The abundance of correspondences offers potential improvements in performance for many computer vision tasks, but also calls for new algorithms to more efficiently utilize them. A recent paper from our research group introduced a method for clustering and summarizing dense matches for essential matrix estimation. This thesis adapts that technique to the estimation of plane normals, including local optimization and bundle adjustment. Our formulation reduces computational cost without sacrificing the quality of the reconstruction. Although our system processes dense matches efficiently, it does not yet match the accuracy of state-of-the-art SLAM pipelines. Still, it demonstrates the potential of plane-based representations for robust and efficient scene reconstruction.