Lightweight Monocular Depth Estimation

• URL: http://arxiv.org/abs/2212.11363v1
• Date: Wed, 21 Dec 2022 21:05:16 GMT
• Title: Lightweight Monocular Depth Estimation
• Authors: Ruilin Ma, Shiyao Chen, Qin Zhang
• Abstract summary: We 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. The proposed method achieves relatively high accuracy and low rootmean-square error.
• Score: 4.19709743271943
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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