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Range-Intensity-Profile-Guided Gated Light Ranging and Imaging Based on a Convolutional Neural Network.

Chenhao Xia^1,2, Xinwei Wang^1,2,3, Liang Sun¹

¹Optoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

This study introduces a novel range-intensity-profile-guided method for 3D depth recovery in gated imaging. The approach effectively uses synthetic data and a convolutional neural network to improve depth estimation accuracy.

Keywords:

deep learning depth recovery light ranging and imaging range-gated imaging range-intensity profile

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Area of Science:

Optics and Photonics
Computer Vision
Machine Learning

Background:

Three-dimensional (3D) range-gated imaging provides high-resolution intensity and depth data.
Existing depth recovery algorithms, like range-intensity correlation and deep learning, face challenges with data requirements or profile generation.
Accurate depth information is crucial for various imaging applications.

Purpose of the Study:

To develop a novel range-intensity-profile-guided method for accurate depth recovery from gated images.
To overcome limitations of existing algorithms by leveraging synthetic data and a specialized neural network.
To improve the performance of depth estimation in 3D range-gated imaging systems.

Main Methods:

A convolutional neural network, termed range-intensity ratio and semantic network (RIRS-net), was developed.
Synthetic training data was generated using Grand Theft Auto V, guided by the system's range-intensity profile (RIP).
The RIRS-net was trained on synthetic data and fine-tuned with RIP data, learning both semantic and range-intensity depth cues.

Main Results:

The proposed method demonstrated superior performance in depth recovery compared to existing algorithms.
Evaluation experiments on both real-scene and synthetic datasets confirmed the method's effectiveness.
The network successfully integrated semantic and range-intensity depth cues for enhanced accuracy.

Conclusions:

The range-intensity-profile-guided approach offers a robust solution for depth recovery in 3D range-gated imaging.
Utilizing synthetic data generation significantly reduces the need for extensive real-world training data.
This method advances the capabilities of gated light ranging and imaging systems.