Lightweight Monocular Depth Estimation

TL;DR

This paper introduces a lightweight monocular depth estimation model based on a U-Net architecture, achieving high accuracy and low error in predicting scene depth from single RGB images.

Contribution

It presents a novel lightweight neural network model for monocular depth estimation using U-Net, optimized for efficiency and accuracy.

Findings

Achieves high accuracy on NYU Depth V2 dataset

Low root-mean-square error in depth prediction

Outperforms some existing lightweight methods

Abstract

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.