Enhancing LiDAR performance using threshold photon-number-resolving detection.

Area of Science:

• Optics and Photonics
• Computer Vision
• Remote Sensing

Background:

• Single-photon Light Detection and Ranging (LiDAR) is crucial for 3D scene reconstruction.
• Standard LiDAR methods face noise issues, degrading depth and reflectivity map quality.
• Existing threshold techniques improve noise but lose information, especially in low-signal environments.

Purpose of the Study:

• To propose a novel LiDAR detection scheme enhancing performance by combining noise suppression and signal amplification.
• To improve 3D scene reconstruction accuracy and map quality in challenging conditions.
• To overcome limitations of existing single-photon and threshold detection methods.

Main Methods:

• Developed a hybrid detection scheme integrating thresholding with photon-number-resolving capabilities.
• Evaluated performance using signal-to-noise ratio (SNR), detection rate, and false alarm rate.
• Validated the scheme through extensive simulations and real-world experiments.

Main Results:

• The proposed scheme significantly enhances LiDAR performance compared to single-photon and threshold methods.
• Achieved superior depth and reflectivity map reconstruction, particularly in high-noise, low-signal regimes.
• Demonstrated improved SNR, detection rates, and reduced false alarm rates.

Conclusions:

• The novel LiDAR detection scheme offers superior performance for 3D scene reconstruction.
• This advancement enables the development of more efficient and lower-noise LiDAR systems.
• The findings are critical for applications requiring high-fidelity 3D data acquisition.