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.
@article{arxiv.2506.03747,
title = {Fast Non-Line-of-Sight Transient Data Simulation and an Open Benchmark Dataset},
author = {Yingjie Shi and Jinye Miao and Taotao Qin and Fuyao Cai and Yi Wei and Lingfeng Liu and Tongyao Li and Chenyang Wu and Huan Liang and Yuyang Yin and Lianfa Bai and Enlai Guo and Jing Han},
journal= {arXiv preprint arXiv:2506.03747},
year = {2025}
}