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Pose Estimation of Buried Deep-Sea Objects using 3D Vision Deep Learning Models

2024-10-01 20:43:20
Jerry Yan, Chinmay Talegaonkar, Nicholas Antipa, Eric Terrill, Sophia Merrifield

Abstract

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.

Abstract (translated)

我们提出了一个用于海底南方加州圣佩德罗盆地中 debris field 油桶残骸的姿势和埋葬分数估计的方法。我们的计算工作流程利用了用于分割的基础模型以及基于视觉 transformer 的方法来估计点云,该点云定义了油桶的形状。我们提出了 BarrelNet,用于从油桶点云中估计 6 自由度油桶的姿势和半径。我们使用由同构生成油桶点云进行训练,并通过历史垃圾填埋场的遥控器视频录像来演示我们方法的潜力。我们将我们的方法与传统的最小二乘法方法进行了比较,并证明了根据我们定义的基准,我们的方法具有显著的改进。

URL

https://arxiv.org/abs/2410.01061

PDF

https://arxiv.org/pdf/2410.01061.pdf


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