Fast Non-Line-of-Sight Transient Data Simulation and an Open Benchmark Dataset

TL;DR

This paper introduces a fast, parameterized simulation method for NLOS transient data that reduces hardware reliance and computational costs, facilitating research and system design in hidden object imaging.

Contribution

It presents a novel, efficient simulation approach modeling light transport for NLOS imaging, along with an open benchmark dataset to advance the field.

Findings

Simulated data accurately reconstructs hidden geometries

Method significantly reduces computational time

Supports rapid scenario configuration and system optimization

Abstract

Non-Line-of-Sight (NLOS) imaging reconstructs the shape and depth of hidden objects from picosecond-resolved transient signals, offering potential applications in autonomous driving, security, and medical diagnostics. However, current NLOS experiments rely on expensive hardware and complex system alignment, limiting their scalability. This manuscript presents a simplified simulation method that generates NLOS transient data by modeling light-intensity transport rather than performing conventional path tracing, significantly enhancing computational efficiency. All scene elements, including the relay surface, hidden target, stand-off distance, detector time resolution, and acquisition window are fully parameterized, allowing for rapid configuration of test scenarios. Reconstructions based on the simulated data accurately recover hidden geometries, validating the effectiveness of the approach. The proposed tool reduces the entry barrier for NLOS research and supports the optimization of system design.