3D Reconstruction via Incremental Structure From Motion

TL;DR

This paper presents a detailed implementation of incremental Structure from Motion (SfM) for accurate 3D reconstruction from unstructured image collections, emphasizing geometric consistency and iterative refinement.

Contribution

It offers a comprehensive pipeline for incremental SfM, demonstrating its effectiveness in sparse 3D reconstruction with real datasets and quality assessment metrics.

Findings

Incremental SfM effectively reconstructs scenes with sparse or partially overlapping images.

Iterative bundle adjustment improves geometric accuracy and camera trajectory coherence.

The approach is practical and reliable for real-world 3D reconstruction tasks.

Abstract

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.