增量式三维重建在稀疏图像下仍能稳定恢复场景结构。
3D Reconstruction via Incremental Structure From Motion
- 逐次加入视角，逐步构建三维模型
- 通过束调整提升几何一致性，降低重投影误差
- 适合光照变化大或数据不全的现实场景
从无序图像集合中实现精确三维重建是机器人、地图构建和场景理解等应用的关键需求。尽管全局SfM依赖完整的图像连接性，对噪声和缺失数据敏感，而增量式SfM则更具灵活性。通过逐次引入新视角，该方法可在稀疏或部分重叠的数据集上恢复场景结构与相机运动。本文详细实现了增量式SfM流程，重点关注几何估计的一致性及束调整带来的迭代优化效果。使用真实数据集进行验证，通过重投影误差和相机轨迹一致性评估重建质量。结果表明，增量式SfM在视觉结构化环境中是一种可靠且实用的稀疏三维重建方法。
原文摘要 · Abstract (English)
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.
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