@inproceedings{72defdcb90414cf8b362d38598a6446e,
title = "Inpainting Sparse Scenes through Physics Aware Transformers for Single-Photon LiDAR",
abstract = "We increase single-photon LiDAR capabilities via a hardware-accelerating inpainting transformer model. This model reconstructs all non-observed information within the image plane as it communicates with the beam steering hardware. We apply this to 3D time-of-flight (ToF) reconstruction, where objects obstruct each other{\textquoteright}s line of sight. We use ToF histograms to distinguish objects within either the foreground and background, and their overlap will be treated as the dynamic mask for the model to reconstruct. We also employ this to unorthodox scanning patterns such as Lissajous and spiral, which are riddled with sparsity. Lastly, we are developing an AI MEMs system, which intelligently downsamples the image plane based off foreground masks, combating sampling redundancy. We believe that our approach will be useful in applications for imaging and sensing dynamic targets with sparse single-photon data across all domains.",
keywords = "Artificial Intelligent, Machine Vision, Quantum Optics, Sparse Imaging, Transformers",
author = "Luke McEvoy and Daniel Tafone and Sua, {Yong Meng} and Yuping Huang",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Unconventional Optical Imaging IV 2024 ; Conference date: 08-04-2024 Through 11-04-2024",
year = "2024",
doi = "10.1117/12.3014641",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
editor = "Irene Georgakoudi and Georges, {Marc P.} and Nicolas Verrier",
booktitle = "Unconventional Optical Imaging IV",
}