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Found 5 papers, 1 papers with code
Photon-Efficient 3D Imaging with A Non-Local Neural Network
Photon-efficient imaging has enabled a number of applications relying on single-photon sensors that can capture a 3D image with as few as one photon per pixel.
NLOST: Non-Line-of-Sight Imaging With Transformer
Specifically, after extracting the shallow features with the assistance of physics-based priors, we design two spatial-temporal self attention encoders to explore both local and global correlations within 3D NLOS data by splitting or downsampling the features into different scales, respectively.
Physics to the Rescue: Deep Non-line-of-sight Reconstruction for High-speed Imaging
Computational approach to imaging around the corner, or non-line-of-sight (NLOS) imaging, is becoming a reality thanks to major advances in imaging hardware and reconstruction algorithms.
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo
Interestingly, we find that, although a stereo matching network trained with the photometric loss is not optimal, its feature extractor can produce degradation-agnostic and matching-specific features.
Towards Non-Line-of-Sight Photography
Non-line-of-sight (NLOS) imaging is based on capturing the multi-bounce indirect reflections from the hidden objects.
