PDF(Pubmed)
Abstract：
Imagery has become one of the main data sources for investigating seascape spatial patterns. This is particularly true in deep-sea environments, which are only accessible with underwater vehicles. On the one hand, using collaborative web-based tools and machine learning algorithms, biological and geological features can now be massively annotated on 2D images with the support of experts. On the other hand, geomorphometrics such as slope or rugosity derived from 3D models built with structure from motion (sfm) methodology can then be used to answer spatial distribution questions. However, precise georeferencing of 2D annotations on 3D models has proven challenging for deep-sea images, due to a large mismatch between navigation obtained from underwater vehicles and the reprojected navigation computed in the process of building 3D models. In addition, although 3D models can be directly annotated, the process becomes challenging due to the low resolution of textures and the large size of the models. In this article, we propose a streamlined, open-access processing pipeline to reproject 2D image annotations onto 3D models using ray tracing. Using four underwater image datasets, we assessed the accuracy of annotation reprojection on 3D models and achieved successful georeferencing to centimetric accuracy. The combination of photogrammetric 3D models and accurate 2D annotations would allow the construction of a 3D representation of the landscape and could provide new insights into understanding species microdistribution and biotic interactions.
摘要：
影像已成为调查海景空间格局的主要数据源之一。在深海环境中尤其如此，只有水下航行器才能进入。一方面,使用基于Web的协作工具和机器学习算法，生物和地质特征现在可以在专家的支持下在2D图像上进行大量注释。另一方面,然后，可以使用从运动结构（sfm）方法构建的3D模型中得出的坡度或皱褶度等地貌计量学来回答空间分布问题。然而,3D模型上2D注释的精确地理参考已被证明对深海图像具有挑战性，由于从水下航行器获得的导航与在构建3D模型的过程中计算的重新投影导航之间存在很大的不匹配。此外,虽然3D模型可以直接注释，由于纹理的低分辨率和模型的大尺寸，该过程变得具有挑战性。在这篇文章中,我们提出了一个精简的，开放访问处理管道，以使用光线跟踪将2D图像注释重新投影到3D模型上。使用四个水下图像数据集，我们评估了3D模型上注释重投影的准确性，并成功实现了厘米精度的地理参考。摄影测量3D模型和准确的2D注释的结合将允许构建景观的3D表示，并可以提供新的见解来理解物种微分布和生物相互作用。
