The paper proposes a novel approach for Non-Line-of-Sight (NLoS) imaging using an indirect Time-of-Flight (iToF) sensor. The key contributions are:
The "mirror trick" - a technique to reframe the NLoS problem as a Line-of-Sight (LoS) one by interpreting the front wall as a virtual mirror. This simplifies the task for the deep learning model and the ground truth data generation.
A deep learning model that takes the raw iToF measurements as input and predicts the iToF data corresponding to the LoS scene obtained by applying the mirror trick. From this output, the depth map of the hidden object can be easily extracted.
The first synthetic dataset for NLoS imaging using iToF data, which is used to train and evaluate the proposed deep learning approach.
The experiments show that the model can recover the overall shape of the hidden object with a mean IoU of 0.77 and estimate the depth with a mean absolute error of 5.21 cm. The authors also perform an ablation study to demonstrate the importance of the key design choices.
To Another Language
from source content
arxiv.org
Djupare frågor