# VIO-Aided Structure from Motion Under Challenging Environments

arXiv: https://arxiv.org/abs/2101.09657 · HTML: https://mlsanity.com/paper/2101.09657/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models](https://mlsanity.com/paper/1810.11692/) (2018)
- [Domain-Size Pooling in Local Descriptors: DSP-SIFT](https://mlsanity.com/paper/1412.8556/) (2014)
- [Stomach 3D Reconstruction Based on Virtual Chromoendoscopic Image Generation](https://mlsanity.com/paper/2004.12288/) (2020)
- [An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM](https://mlsanity.com/paper/1903.08636/) (2019)
- [3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint](https://mlsanity.com/paper/2006.10383/) (2020)

## Built on by (most cited first, 2 of 2)

- [AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from Motion](https://mlsanity.com/paper/2301.12135/) (2023)
- [Video-Based Camera Localization Using Anchor View Detection and Recursive 3D Reconstruction](https://mlsanity.com/paper/2107.03068/) (2021)

## Abstract (arXiv)

In this paper, we present a robust and efficient Structure from Motion pipeline for accurate 3D reconstruction under challenging environments by leveraging the camera pose information from a visual-inertial odometry. Specifically, we propose a geometric verification method to filter out mismatches by considering the prior geometric configuration of candidate image pairs. Furthermore, we introduce an efficient and scalable reconstruction approach that relies on batched image registration and robust bundle adjustment, both leveraging the reliable local odometry estimation. Extensive experimental results show that our pipeline performs better than the state-of-the-art SfM approaches in terms of reconstruction accuracy and robustness for challenging sequential image collections.