用视觉模型估算海底物体埋深，助力污染评估与环境监测
PoseIDON: 6DoF Pose Estimation with Foundation Model Features for Marine Sediment Burial Mapping
- 结合大模型特征与多视角建模，估计物体姿态和海底朝向
- 在54个目标上实现平均埋深误差约10厘米
- 适合海洋污染监测与环境风险评估人员使用
人类活动遗留物在海床的埋藏状态可揭示局部沉积动态，对评估生态风险、污染物迁移及危险物质（如弹药）的回收或缓解策略至关重要。由于部分遮挡、能见度差和物体退化，仅靠远程影像准确估计埋深仍具挑战。本文提出一种计算机视觉流程PoseIDON，融合深度基础模型特征与多视角摄影测量技术，从遥控潜水器（ROV）视频中估计物体六自由度姿态及周围海床朝向。通过将物体的CAD模型与观测图像对齐，并拟合局部平面海床模型，推断埋深。该方法在圣佩德罗盆地历史海洋倾倒区的54个目标（包括桶装物和弹药）视频上验证，平均埋深误差约为10厘米，可解析出反映沉积过程的空间埋藏模式。该方法支持可扩展、非侵入式海床埋藏制图，有助于污染区域的环境评估。
原文摘要 · Abstract (English)
The burial state of anthropogenic objects on the seafloor provides insight into localized sedimentation dynamics and is also critical for assessing ecological risks, potential pollutant transport, and the viability of recovery or mitigation strategies for hazardous materials such as munitions. Accurate burial depth estimation from remote imagery remains difficult due to partial occlusion, poor visibility, and object degradation. This work introduces a computer vision pipeline, called PoseIDON, which combines deep foundation model features with multiview photogrammetry to estimate six degrees of freedom object pose and the orientation of the surrounding seafloor from ROV video. Burial depth is inferred by aligning CAD models of the objects with observed imagery and fitting a local planar approximation of the seafloor. The method is validated using footage of 54 objects, including barrels and munitions, recorded at a historic ocean dumpsite in the San Pedro Basin. The model achieves a mean burial depth error of approximately 10 centimeters and resolves spatial burial patterns that reflect underlying sediment transport processes. This approach enables scalable, non-invasive mapping of seafloor burial and supports environmental assessment at contaminated sites.
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