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Adaptive Lighting for Data-Driven Non-Line-Of-Sight 3D Localization

Description

Non-line-of-sight (NLOS) imaging of objects not visible to either the camera or illumina-

tion source is a challenging task with vital applications including surveillance and robotics.

Recent NLOS reconstruction advances have been achieved using time-resolved measure-

ments. Acquiring these time-resolved measurements requires expensive and specialized

detectors and laser sources. In work proposes a data-driven…

Non-line-of-sight (NLOS) imaging of objects not visible to either the camera or illumina-

tion source is a challenging task with vital applications including surveillance and robotics.

Recent NLOS reconstruction advances have been achieved using time-resolved measure-

ments. Acquiring these time-resolved measurements requires expensive and specialized

detectors and laser sources. In work proposes a data-driven approach for NLOS 3D local-

ization requiring only a conventional camera and projector. The localisation is performed

using a voxelisation and a regression problem. Accuracy of greater than 90% is achieved

in localizing a NLOS object to a 5cm × 5cm × 5cm volume in real data. By adopting

the regression approach an object of width 10cm to localised to approximately 1.5cm. To

generalize to line-of-sight (LOS) scenes with non-planar surfaces, an adaptive lighting al-

gorithm is adopted. This algorithm, based on radiosity, identifies and illuminates scene

patches in the LOS which most contribute to the NLOS light paths, and can factor in sys-

tem power constraints. Improvements ranging from 6%-15% in accuracy with a non-planar

LOS wall using adaptive lighting is reported, demonstrating the advantage of combining

the physics of light transport with active illumination for data-driven NLOS imaging.

ContributorsChandran, Sreenithy (Author) / Jayasuriya, Suren (Thesis advisor) / Turaga, Pavan (Committee member) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)

Created2019

Neural Fields for Tomographic Imaging: with Applications in X-ray Computed Tomography and Synthetic Aperture Sonar

Description

Computed tomography (CT) and synthetic aperture sonar (SAS) are tomographic imaging techniques that are fundamental for applications within medical and remote sensing. Despite their successes, a number of factors constrain their image quality. For example, a time-varying scene during measurement acquisition yields image artifacts. Additionally, factors such as bandlimited or…

Computed tomography (CT) and synthetic aperture sonar (SAS) are tomographic imaging techniques that are fundamental for applications within medical and remote sensing. Despite their successes, a number of factors constrain their image quality. For example, a time-varying scene during measurement acquisition yields image artifacts. Additionally, factors such as bandlimited or sparse measurements limit image resolution. This thesis presents novel algorithms and techniques to account for these factors during image formation and outperform traditional reconstruction methods. In particular, this thesis formulates analysis-by-synthesis optimizations that leverage neural fields to predict the scene and differentiable physics models that incorporate prior knowledge about image formation. The specific contributions include: (1) a method for reconstructing CT measurements from time-varying (non-stationary) scenes; (2) a method for deconvolving SAS images, which benefits image quality; (3) a method that couples neural fields and a differentiable acoustic model for 3D SAS reconstructions.

ContributorsReed, Albert William (Author) / Jayasuriya, Suren (Thesis advisor) / Brown, Daniel C (Committee member) / Dasarathy, Gautam (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)

Created2023