3D Reconstruction via Incremental Structure From Motion
This work addresses 3D reconstruction for applications like robotics and mapping, but it is incremental as it focuses on a detailed implementation of an existing method.
The paper tackled the problem of 3D reconstruction from unstructured image collections by implementing an incremental Structure from Motion pipeline, demonstrating its utility with real data through metrics like reprojection error and camera trajectory coherence.
Accurate 3D reconstruction from unstructured image collections is a key requirement in applications such as robotics, mapping, and scene understanding. While global Structure from Motion (SfM) techniques rely on full image connectivity and can be sensitive to noise or missing data, incremental SfM offers a more flexible alternative. By progressively incorporating new views into the reconstruction, it enables the system to recover scene structure and camera motion even in sparse or partially overlapping datasets. In this paper, we present a detailed implementation of the incremental SfM pipeline, focusing on the consistency of geometric estimation and the effect of iterative refinement through bundle adjustment. We demonstrate the approach using a real dataset and assess reconstruction quality through reprojection error and camera trajectory coherence. The results support the practical utility of incremental SfM as a reliable method for sparse 3D reconstruction in visually structured environments.