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Scale Accuracy Evaluation of Image-Based 3D Reconstruction Strategies Using Laser Photogrammetry

DOI:10.3390/rs11182093 期刊:Remote Sensing 出版年份:2019 更新时间:2025-09-11 14:15:04
摘要: Rapid developments in the field of underwater photogrammetry have given scientists the ability to produce accurate 3-dimensional (3D) models which are now increasingly used in the representation and study of local areas of interest. This paper addresses the lack of systematic analysis of 3D reconstruction and navigation fusion strategies, as well as associated error evaluation of models produced at larger scales in GPS-denied environments using a monocular camera (often in deep sea scenarios). Based on our prior work on automatic scale estimation of Structure from Motion (SfM)-based 3D models using laser scalers, an automatic scale accuracy framework is presented. The confidence level for each of the scale error estimates is independently assessed through the propagation of the uncertainties associated with image features and laser spot detections using a Monte Carlo simulation. The number of iterations used in the simulation was validated through the analysis of the final estimate behavior. To facilitate the detection and uncertainty estimation of even greatly attenuated laser beams, an automatic laser spot detection method was developed, with the main novelty of estimating the uncertainties based on the recovered characteristic shapes of laser spots with radially decreasing intensities. The effects of four different reconstruction strategies resulting from the combinations of Incremental/Global SfM, and the a priori and a posteriori use of navigation data were analyzed using two distinct survey scenarios captured during the SUBSAINTES 2017 cruise (doi: 10.17600/17001000). The study demonstrates that surveys with multiple overlaps of nonsequential images result in a nearly identical solution regardless of the strategy (SfM or navigation fusion), while surveys with weakly connected sequentially acquired images are prone to produce broad-scale deformation (doming effect) when navigation is not included in the optimization. Thus the scenarios with complex survey patterns substantially benefit from using multiobjective BA navigation fusion. The errors in models, produced by the most appropriate strategy, were estimated at around 1% in the central parts and always inferior to 5% on the extremities. The effects of combining data from multiple surveys were also evaluated. The introduction of additional vectors in the optimization of multisurvey problems successfully accounted for offset changes present in the underwater USBL-based navigation data, and thus minimize the effect of contradicting navigation priors. Our results also illustrate the importance of collecting a multitude of evaluation data at different locations and moments during the survey.
作者: Klemen Isteniˇc,Rafael Garcia,Nuno Gracias,Aurélien Arnaubec,Javier Escartín
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To present and use an automatic scale accuracy estimation framework, applicable to models reconstructed from optical imagery and associated navigation data, and to evaluate various reconstruction strategies often used in research and industrial ROV deep sea surveys.

The study demonstrates the effectiveness of an automatic scale accuracy estimation framework for underwater 3D models, highlighting the importance of navigation fusion strategies in minimizing scale drift and deformation. The most appropriate strategy produced models with errors around 1% in central parts and less than 5% on extremities. The research underscores the significance of collecting evaluation data at various locations and times during surveys to ensure model accuracy.

The study is limited by the challenges of underwater image acquisition, including light attenuation and scattering, and the need for additional information to disambiguate scale in monocular camera reconstructions. The accuracy of the models is contingent on the quality of the input data and the strategy used in the fusion of image and navigation information.

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