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Related Experiment Video

Updated: Aug 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Monocular depth estimation network with single-pixel depth guidance.

Hongjae Lee, Jinbum Park, Wooseok Jeong

|February 1, 2023

Summary

This summary is machine-generated.

This study introduces PhoMoNet, a novel monocular depth estimation (MDE) network. PhoMoNet utilizes cost-effective single-pixel depth guidance to overcome MDE scale ambiguity, achieving superior performance on synthetic and real-world data.

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Area of Science:

Computer Vision
Machine Learning
Robotics

Background:

Monocular depth estimation (MDE) is limited by scale ambiguity.
Multi-camera systems improve MDE but increase hardware costs.
Active depth cameras offer solutions but are expensive and bulky.

Purpose of the Study:

To develop a cost-effective solution for MDE scale ambiguity.
To introduce a novel MDE network using single-pixel depth guidance.
To enhance MDE performance without complex hardware.

Main Methods:

Designed a single-pixel guidance module (SPGM) integrating global depth and local image features.
Developed PhoMoNet, an end-to-end MDE network incorporating SPGMs.
Utilized a single-photon avalanche diode for depth guidance.

Main Results:

PhoMoNet effectively combines single-pixel depth guidance with image features.
The proposed method overcomes inherent scale ambiguity in MDE.
Experimental results demonstrate PhoMoNet's superiority over state-of-the-art MDE networks.

Conclusions:

PhoMoNet offers a cost-effective and efficient approach to MDE.
Single-pixel depth guidance is a viable alternative for improving MDE accuracy.
The developed network shows strong performance on diverse datasets.