利用单目深度大模型监督雷达深度完井。

Applied optics Pub Date : 2025-09-20 DOI:10.1364/AO.569559
Jimin Chen, Zili Zhou, Zhu Yu, Fuyi Zhang, Jiacheng Ying, Si-Yuan Cao, Hui-Liang Shen
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引用次数: 0

摘要

近年来,雷达深度完井在开发骨干网络和高质量数据集方面取得了重大进展。然而,如何优化监管方式却鲜有人关注。在这项工作中,我们提出了一种新的监督方法,据我们所知,使用相对度量转换(R2MC)模块来利用单目深度大模型(MDLM)的泛化能力。R2MC模块采用稀疏LiDAR数据,通过像素级局部映射获得公制深度尺度,同时保留MDLM的泛化能力。实验结果表明,我们的R2MC模块可以与不同的主干网相结合,与原有的监控方式相比,可以提高主干网的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Supervising radar depth completion using the monocular depth large model.

In recent years, radar depth completion has made significant advances in developing backbone networks and high-quality datasets. However, less attention has been paid to optimizing the supervision manner. In this work, we propose a novel supervision method, to the best of our knowledge, using a relative-to-metric conversion (R2MC) module to leverage the generalization capability of the monocular depth large model (MDLM). The R2MC module employs sparse LiDAR data to obtain metric depth scales through pixelwise local mapping while preserving the generalization capability of the MDLM. The experimental results illustrate that our R2MC module can be combined with different backbones and improve their performance compared to their original supervision manners.

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