用3D视觉深度学习估算深海掩埋桶的朝向与埋深。
Pose Estimation of Buried Deep-Sea Objects using 3D Vision Deep Learning Models
- 基于视觉变换器生成桶的点云，再用新模型估算6自由度姿态和半径。
- 在合成数据上训练，实测视频中对埋深估计误差显著优于传统方法。
- 适合海洋废弃物监测、深海机器人导航等场景使用。
本文提出一种方法，用于估算位于南加州圣佩德罗盆地海底废弃物场中油桶的位姿与埋藏比例。该计算流程利用最新的分割基础模型及基于视觉变压器的点云重建方法，生成定义油桶几何结构的点云数据。我们提出BarrelNet模型，输入为油桶点云，输出其6-自由度姿态和半径。模型在合成生成的油桶点云上进行训练，并通过遥控无人潜水器（ROV）在历史倾倒点获取的视频影像进行定性验证。与传统的最小二乘拟合方法相比，本方法在设定的评估基准下表现显著更优。
原文摘要 · Abstract (English)
We present an approach for pose and burial fraction estimation of debris field barrels found on the seabed in the Southern California San Pedro Basin. Our computational workflow leverages recent advances in foundation models for segmentation and a vision transformer-based approach to estimate the point cloud which defines the geometry of the barrel. We propose BarrelNet for estimating the 6-DOF pose and radius of buried barrels from the barrel point clouds as input. We train BarrelNet using synthetically generated barrel point clouds, and qualitatively demonstrate the potential of our approach using remotely operated vehicle (ROV) video footage of barrels found at a historic dump site. We compare our method to a traditional least squares fitting approach and show significant improvement according to our defined benchmarks.
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