Computational study demonstrates neural reconstruction of hidden scenes in occluded non-line-of-sight settings, suggesting improved visibility feature extraction.
Non-Line-of-Sight Imaging (NLOS) addresses the challenge of capturing a scene which is not directly visible from a sensor line-of-sight, obscured by an occluder. This work uses time-of-flight formulation to propose a learning-based method that represents implicitly the hidden scene partially disambiguated by transient imaging and explores new visibility features coherent with this representation.
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Rodriguez-Sanz et al. (2024) studied this question.
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