Lightweight Monocular Depth Estimation
This work addresses depth estimation for applications like robotics and self-driving cars, but it appears incremental as it builds on existing U-Net structures without major innovations.
The paper tackles monocular depth estimation from single RGB images using a lightweight U-Net model, achieving relatively high accuracy and low root mean square error on the NYU Depth V2 dataset.
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.