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Found 8 papers, 7 papers with code
CompenHR: Efficient Full Compensation for High-resolution Projector
Full projector compensation is a practical task of projector-camera systems.
Modeling Deep Learning Based Privacy Attacks on Physical Mail
Mail privacy protection aims to prevent unauthorized access to hidden content within an envelope since normal paper envelopes are not as safe as we think.
SPAA: Stealthy Projector-based Adversarial Attacks on Deep Image Classifiers
Light-based adversarial attacks use spatial augmented reality (SAR) techniques to fool image classifiers by altering the physical light condition with a controllable light source, e. g., a projector.
End-to-end Full Projector Compensation
Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface.
DeProCams: Simultaneous Relighting, Compensation and Shape Reconstruction for Projector-Camera Systems
In this paper, we propose a novel end-to-end trainable model named DeProCams to explicitly learn the photometric and geometric mappings of ProCams, and once trained, DeProCams can be applied simultaneously to the three tasks.
CompenNet++: End-to-end Full Projector Compensation
In this paper, we propose the first end-to-end solution, named CompenNet++, to solve the two problems jointly.
End-to-end Projector Photometric Compensation
Such benchmark is not previously available, to our best knowledge, due to the fact that conventional evaluation requests the hardware system to actually project the final results.
A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets
Existing camera-projector calibration methods typically warp feature points from a camera image to a projector image using estimated homographies, and often suffer from errors in camera parameters and noise due to imperfect planarity of the calibration target.
