A1 Refereed original research article in a scientific journal
Building Better Models: Benchmarking Feature Extraction and Matching for Structure from Motion at Construction Sites
Authors: Cueto Zumaya, Carlos Roberto; Catalano, Iacopo; Peña Queralta, Jorge
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
Publication year: 2024
Journal: Remote Sensing
Journal name in source: Remote Sensing
Article number: 2974
Volume: 16
Issue: 16
eISSN: 2072-4292
DOI: https://doi.org/10.3390/rs16162974
Web address : https://doi.org/10.3390/rs16162974
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/457763687
The popularity of Structure from Motion (SfM) techniques has significantly advanced 3D reconstruction in various domains, including construction site mapping. Central to SfM, is the feature extraction and matching process, which identifies and correlates keypoints across images. Previous benchmarks have assessed traditional and learning-based methods for these tasks but have not specifically focused on construction sites, often evaluating isolated components of the SfM pipeline. This study provides a comprehensive evaluation of traditional methods (e.g., SIFT, AKAZE, ORB) and learning-based methods (e.g., D2-Net, DISK, R2D2, SuperPoint, SOSNet) within the SfM pipeline for construction site mapping. It also compares matching techniques, including SuperGlue and LightGlue, against traditional approaches such as nearest neighbor. Our findings demonstrate that deep learning-based methods such as DISK with LightGlue and SuperPoint with various matchers consistently outperform traditional methods like SIFT in both reconstruction quality and computational efficiency. Overall, the deep learning methods exhibited better adaptability to complex construction environments, leveraging modern hardware effectively, highlighting their potential for large-scale and real-time applications in construction site mapping. This benchmark aims to assist researchers in selecting the optimal combination of feature extraction and matching methods for SfM applications at construction sites.
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Funding information in the publication:
This research received no external funding.
