In the rapidly evolving field of computer vision, propelled by advancements in deeplearning, the integration of hardware-software co-design has become crucial to overcome the limitations of traditional imaging systems. This dissertation explores the integration of hardware-software co-design in computational imaging, particularly in light transport acquisition and Non-Line-of-Sight (NLOS) imaging. By leveraging projector-camera systems and computational techniques, this thesis address critical challenges in imaging complex environments, such as adverse weather conditions, low-light scenarios, and the imaging of reflective or transparent objects. The first contribution in this thesis is the theory, design, and implementation of a slope disparity gating system, which is a vertically aligned configuration of a synchronized raster scanning projector and rolling-shutter camera, facilitating selective imaging through disparity-based triangulation. This system introduces a novel, hardware-oriented approach to selective imaging, circumventing the limitations of post-capture processing. The second contribution of this thesis is the realization of two innovative approaches for spotlight optimization to improve localization and tracking for NLOS imaging. The first approach utilizes radiosity-based optimization to improve 3D localization and object identification for small-scale laboratory settings. The second approach introduces a learningbased illumination network along with a differentiable renderer and NLOS estimation network to optimize human 2D localization and activity recognition. This approach is validated on a large, room-scale scene with complex line-of-sight geometries and occluders. The third contribution of this thesis is an attention-based neural network for passive NLOS settings where there is no controllable illumination. The thesis demonstrates realtime, dynamic NLOS human tracking where the camera is moving on a mobile robotic platform. In addition, this thesis contains an appendix featuring temporally consistent relighting for portrait videos with applications in computer graphics and vision.

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.