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Found 9 papers, 2 papers with code
Bayesian Based Unrolling for Reconstruction and Super-resolution of Single-Photon Lidar Systems
Deploying 3D single-photon Lidar imaging in real world applications faces several challenges due to imaging in high noise environments and with sensors having limited resolution.
3D Target Detection and Spectral Classification for Single-photon LiDAR Data
3D single-photon LiDAR imaging has an important role in many applications.
A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems
Deploying 3D single-photon Lidar imaging in real world applications faces multiple challenges including imaging in high noise environments.
Real-time, low-cost multi-person 3D pose estimation
The process of tracking human anatomy in computer vision is referred to pose estimation, and it is used in fields ranging from gaming to surveillance.
Fast Task-Based Adaptive Sampling for 3D Single-Photon Multispectral Lidar Data
Given a task of interest, the iterative sampling strategy targets the most informative regions of a scene which are defined as those minimizing parameter uncertainties.
High-speed object detection with a single-photon time-of-flight image sensor
The results, obtained with exposure times down to 2 ms (equivalent to 500 FPS) and in signal-to-background (SBR) ratios as low as 0. 05, point to the advantages of providing the CNN with full histogram data rather than point clouds alone.
Robust and Guided Bayesian Reconstruction of Single-Photon 3D Lidar Data: Application to Multispectral and Underwater Imaging
3D Lidar imaging can be a challenging modality when using multiple wavelengths, or when imaging in high noise environments (e. g., imaging through obscurants).
Robust super-resolution depth imaging via a multi-feature fusion deep network
The network is designed for a SPAD camera operating in a dual-mode such that it captures alternate low resolution depth and high resolution intensity images at high frame rates, thus the system does not require any additional sensor to provide intensity images.
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem.
