开放水域:水下计算机视觉的逼真模拟

Mehdi Mousavi, Shardul Vaidya, Razat Sutradhar, A. Ashok
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引用次数: 2

摘要

在本文中,我们提出了OpenWaters,一个实时的开源水下模拟套件,用于生成逼真的水下场景。OpenWaters通过模拟不同的现实世界条件,支持创建大量的水下图像。它允许对模拟实例中的每个变量进行精细控制,包括几何形状、渲染参数(如光线跟踪的水焦散、散射和地面真值标签)。使用水下深度(相机与物体之间的距离)估计作为用例,我们展示并验证了OpenWaters建模水下场景的能力,这些场景用于训练深度神经网络进行深度估计。我们的实验评估证明了使用合成水下图像进行深度估计具有很高的精度,以及从合成图像到真实图像的特征迁移学习的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OpenWaters: Photorealistic Simulations For Underwater Computer Vision
In this paper, we present OpenWaters, a real-time open-source underwater simulation kit for generating photorealistic underwater scenes. OpenWaters supports creation of massive amount of underwater images by emulating diverse real-world conditions. It allows for fine controls over every variable in a simulation instance, including geometry, rendering parameters like ray-traced water caustics, scattering, and ground-truth labels. Using underwater depth (distance between camera and object) estimation as the use-case, we showcase and validate the capabilities of OpenWaters to model underwater scenes that are used to train a deep neural network for depth estimation. Our experimental evaluation demonstrates depth estimation using synthetic underwater images with high accuracy, and feasibility of transfer-learning of features from synthetic to real-world images.
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