Open AccessNon-fusion time-resolved depth image reconstruction using a highly efficient neural network architecture
Author(s) -
Zhenya Zang,
Dong Xiao,
David Li
Publication year - 2021
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.425917
Subject(s) - ranging , computer science , artificial neural network , artificial intelligence , iterative reconstruction , lidar , computer vision , quantization (signal processing) , optics , physics , telecommunications
Single-photon avalanche diodes (SPAD) are powerful sensors for 3D light detection and ranging (LiDAR) in low light scenarios due to their single-photon sensitivity. However, accurately retrieving ranging information from noisy time-of-arrival (ToA) point clouds remains a challenge. This paper proposes a photon-efficient, non-fusion neural network architecture that can directly reconstruct high-fidelity depth images from ToA data without relying on other guiding images. Besides, the neural network architecture was compressed via a low-bit quantization scheme so that it is suitable to be implemented on embedded hardware platforms. The proposed quantized neural network architecture achieves superior reconstruction accuracy and fewer parameters than previously reported networks.