Evaluating error sources to improve precision in the co-registration of underwater 3D models

Change detection is an essential and widely used approach for investigating ecosystem dynamics. Multi-temporal 3D models increasingly underpin photogrammetry-based analyses of change for many ecologically relevant attributes. To detect change, it is necessary to accurately align 3D models collected at different times using a process referred to as co-registration. However, achieving precise co-registration is difficult in underwater habitats due to practical challenges intrinsic to surveying them. These include a lack of accurate georeferencing information, variable light, turbidity and weather conditions, and diving restrictions dictated by the diver's pressure exposure over time. Here we present an efficient co-registration workflow for 3D models that directly addresses these challenges, derived from underwater structure-from-motion methods. To test our approach, we used 3D models from across a wide range of coral reef habitats covering all those that one may encounter in shallow reefs (15m depth and above). We then identified and empirically estimated four key sources of error: co-registration, 3D processing, image acquisition, and reference and scaling features (RSF) placement, and quantified their relative contributions to the overall error. Our proposed co-registration workflow had a mean precision of 1.37±16.55mm. Image acquisition and RSF placement errors contributed the most to the total workflow error (37% and 53%, respectively), while the contribution of co-registration and 3D processing errors was minimal (3% and 7%, respectively). As a result of our analysis, we provide ‘good practice’ guidelines to reduce errors associated with photogrammetric workflows and to facilitate efficient and reliable detection of 3D change in complex underwater ecosystems.This study was conducted as part of the Ecological Intelligence for Reef Restoration and Adaptation Program (EcoRRAP) (https://gbrrestoration.org/program/ecorrap/). Underwater imagery was collected using EcoRRAP 3D photogrammetry techniques described by Gordon et al. (2023).

变化检测是研究生态系统动态的核心且应用广泛的方法。多时序三维模型正日益为诸多生态相关属性的摄影测量变化分析提供支撑。要实现变化检测，需通过被称为共配准（co-registration）的流程，对不同时段采集的三维模型进行精准对齐。然而，由于水下勘测固有的实际难题，在水下生境中实现精准共配准颇具挑战，具体包括缺乏精准地理参考信息、光照、浊度与天气条件多变，以及受潜水员随时间承受的压力暴露所限制的潜水约束。为此，我们提出一种源自水下运动恢复结构（structure-from-motion）方法的高效共配准工作流，可直接应对上述挑战。为验证所提方法，我们使用了涵盖浅礁（水深15米及以上）中可能遇到的所有珊瑚礁生境类型的多组三维模型。随后我们识别并实证估算了四类关键误差来源：共配准误差、三维处理误差、图像采集误差，以及参考与缩放特征（reference and scaling features, RSF）布设误差，并量化了它们对总误差的相对贡献。我们提出的共配准工作流的平均精度为1.37±16.55毫米。图像采集与RSF布设误差对总工作流误差的贡献占比最高，分别为37%与53%；而共配准与三维处理误差的贡献则相对极小，分别仅为3%与7%。基于本研究的分析结果，我们提供了"最佳实践"指南，以降低摄影测量工作流相关误差，助力复杂水下生态系统中三维变化的高效可靠检测。本研究作为珊瑚礁恢复与适应生态智能计划（Ecological Intelligence for Reef Restoration and Adaptation Program, EcoRRAP）的一部分开展（https://gbrrestoration.org/program/ecorrap/）。水下影像采用Gordon等人（2023）所描述的EcoRRAP三维摄影测量技术采集。