# 3D Reconstruction via Incremental Structure From Motion

arXiv: https://arxiv.org/abs/2508.01019 · HTML: https://mlsanity.com/paper/2508.01019/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer](https://mlsanity.com/paper/2208.03543/) (2022)
- [Deep Learning in Robotics: A Review of Recent Research](https://mlsanity.com/paper/1707.07217/) (2017)
- [A Review on Deep Learning in Medical Image Reconstruction](https://mlsanity.com/paper/1906.10643/) (2019)
- [Refractive COLMAP: Refractive Structure-from-Motion Revisited](https://mlsanity.com/paper/2403.08640/) (2024)
- [On Incremental Structure-from-Motion using Lines](https://mlsanity.com/paper/2105.11196/) (2021)
- [AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from Motion](https://mlsanity.com/paper/2301.12135/) (2023)

## Built on by (most cited first, 1 of 1)

- [LiDAR Point Cloud Colourisation Using Multi-Camera Fusion and Low-Light Image Enhancement](https://mlsanity.com/paper/2509.25859/) (2025)

## Abstract (arXiv)

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.