论文标题
轻量级单眼估计
Lightweight Monocular Depth Estimation
论文作者
论文摘要
单眼深度估计可以在解决从2D图像中得出场景几何的问题中起重要作用。它已用于各种行业，包括机器人，自动驾驶汽车，场景理解，3D重建等。我们方法的目的是创建一个轻巧的机器学习模型，以预测每个像素的深度值，仅给出一个单个RGB图像作为输入，并具有图像分割网络的UNET结构。我们使用NYU深度V2数据集测试结构，并将结果与其他方法进行比较。所提出的方法达到了相对较高的精度和较低的root平方误差。
Monocular depth estimation can play an important role in addressing the issue of deriving scene geometry from 2D images. It has been used in a variety of industries, including robots, self-driving cars, scene comprehension, 3D reconstructions, and others. The goal of our method is to create a lightweight machine-learning model in order to predict the depth value of each pixel given only a single RGB image as input with the Unet structure of the image segmentation network. We use the NYU Depth V2 dataset to test the structure and compare the result with other methods. The proposed method achieves relatively high accuracy and low rootmean-square error.
