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Multi-Scale Histogram-Based Probabilistic Deep Neural Network for Super-Resolution 3D LiDAR Imaging.

Miao Sun¹, Shenglong Zhuo¹, Patrick Yin Chiang¹

¹State Key Laboratory of ASIC and System, Fudan University, No. 825, Zhangheng Road, Shanghai 201203, China.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This summary is machine-generated.

This study introduces a novel neural network for Single-Photon Avalanche Diode (SPAD) imaging, reducing hardware costs for precise depth estimation. The method enhances LiDAR imaging by using temporal multi-scale histograms for improved accuracy.

Keywords:

LiDAR imaging SPAD sensor neural network super resolution

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Area of Science:

Photonics and Imaging Technologies
Artificial Intelligence in Sensor Systems
Computational Imaging

Background:

Single-Photon Avalanche Diode (SPAD) imaging, crucial for high-precision LiDAR, faces challenges with large on-chip histogram circuits, increasing area and cost.
Existing methods require significant on-chip hardware for accurate depth measurement, limiting the scalability and affordability of SPAD-based LiDAR systems.

Purpose of the Study:

To develop a probabilistic estimation-based super-resolution neural network to overcome the area penalty in SPAD imaging for LiDAR.
To reduce the on-chip hardware requirements for histogram computation in SPAD sensors.
To achieve high-resolution depth estimation using limited on-chip histogram data.

Main Methods:

A novel probabilistic estimation-based super-resolution neural network was designed, utilizing temporal multi-scale histograms as input for the first time.
A probabilistic encoder was developed to address depth estimation challenges inherent in SPADs, leveraging the statistical distribution of returned photons.
Partial on-chip histogram hardware was implemented, focusing on calculating reflected photons to minimize area and cost.

Main Results:

The proposed neural network successfully enabled 16x up-sampling for depth estimation.
The system utilized 32x32 multi-scale histogram outputs, demonstrating effective depth estimation with reduced hardware.
Laboratory validation with a 32x32 SPAD sensor system confirmed the neural network's effectiveness.

Conclusions:

The developed probabilistic super-resolution neural network offers a viable solution to reduce the area and cost of SPAD-based LiDAR systems.
This approach enables high-precision depth estimation with significantly less on-chip hardware compared to traditional methods.
The findings pave the way for more compact and cost-effective SPAD imaging sensors for various applications.