论文标题
Udepth：视觉引导的水下机器人的快速单眼深度估计
UDepth: Fast Monocular Depth Estimation for Visually-guided Underwater Robots
论文作者
论文摘要
在本文中，我们提出了一种快速的单眼深度估计方法，用于启用低成本水下机器人的3D感知能力。我们制定了一种名为Udepth的新型端到端深层视觉学习管道，该管道结合了自然水下场景的图像形成特征的领域知识。首先，我们通过利用水下光衰减来调整一个新的输入空间，然后先设计最小二乘的配方，以用于粗像素深度的深度预测。随后，我们将其扩展到一个域投影损失，该域损失指导超过9K RGB-D训练样本的Udepth的端到端学习。 Udepth设计采用计算轻型MobilenetV2骨干和基于变压器的优化器设计，以确保嵌入式系统上的快速推理速率。通过域感知的设计选择并通过全面的实验分析，我们证明了可以在确保较小的计算足迹的同时，实现最新的深度估计性能。具体而言，与现有基准相比，网络参数少70％-80％，Udepth实现了可比的，并且通常更高的深度估计性能。虽然整个模型在单个GPU（CPU核心）上提供了超过66 fps（13 fps）的推理率，但我们对粗深度预测的域投影在单板NVIDIA JETSON TX2S上以51.5 fps的速率运行。推理管道可从https://github.com/uf-robopi/udepth获得。
In this paper, we present a fast monocular depth estimation method for enabling 3D perception capabilities of low-cost underwater robots. We formulate a novel end-to-end deep visual learning pipeline named UDepth, which incorporates domain knowledge of image formation characteristics of natural underwater scenes. First, we adapt a new input space from raw RGB image space by exploiting underwater light attenuation prior, and then devise a least-squared formulation for coarse pixel-wise depth prediction. Subsequently, we extend this into a domain projection loss that guides the end-to-end learning of UDepth on over 9K RGB-D training samples. UDepth is designed with a computationally light MobileNetV2 backbone and a Transformer-based optimizer for ensuring fast inference rates on embedded systems. By domain-aware design choices and through comprehensive experimental analyses, we demonstrate that it is possible to achieve state-of-the-art depth estimation performance while ensuring a small computational footprint. Specifically, with 70%-80% less network parameters than existing benchmarks, UDepth achieves comparable and often better depth estimation performance. While the full model offers over 66 FPS (13 FPS) inference rates on a single GPU (CPU core), our domain projection for coarse depth prediction runs at 51.5 FPS rates on single-board NVIDIA Jetson TX2s. The inference pipelines are available at https://github.com/uf-robopi/UDepth.
