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Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface. Traditional methods usually solve the two parts separately and may suffer from suboptimal solutions. In this paper, we propose the first end-to-end differentiable solution, named CompenNeSt++, to solve the two problems jointly. First, we propose a novel geometric correction subnet, named WarpingNet, which is designed with a cascaded coarse-to-fine structure to learn the sampling grid directly from sampling images. Second, we propose a novel photometric compensation subnet, named CompenNeSt, which is designed with a siamese architecture to capture the photometric interactions between the projection surface and the projected images, and to use such information to compensate the geometrically corrected images. By concatenating WarpingNet with CompenNeSt, CompenNeSt++ accomplishes full projector compensation and is end-to-end trainable. Third, to improve practicability, we propose a novel synthetic data-based pre-training strategy to significantly reduce the number of training images and training time. Moreover, we construct the first setup-independent full compensation benchmark to facilitate future studies. In thorough experiments, our method shows clear advantages over prior art with promising compensation quality and meanwhile being practically convenient. [ABSTRACT FROM AUTHOR]

Existing projector-camera calibration methods typically warp keypoints from a camera image to a projector image using estimated homographies and often suffer from errors in camera parameters and noises due to imperfect planarity of the calibration target. This article proposes a practical and robust projector-camera calibration system that explicitly deals with these challenges. First, a graph-theory-based correspondence algorithm is built on top of a color-coded spatial structured light (SL) pattern. Such SL correspondences are then used for a coarse projector-camera calibration. To gain more robustness against noises from an imperfect planar calibration board, we develop a bundle adjustment algorithm to jointly optimize the estimated projector-camera parameters and the correspondences’ coordinates. Moreover, our system requires only one shot of an SL pattern for each calibration board pose, which is much more practical than multishot solutions. Comprehensive experimental validation is conducted on both synthetic and real data sets, and our method clearly outperforms the existing methods in all experiments. For the benefit of the society, a practical open-source software with graphical user interface (GUI) of the developed system is publicly available at https://github.com/bingyaohuang/single-shot-pro-cam-calib . Note to Practitioners—The proposed method is motivated by two challenges in industrial structured light (SL) system calibration: 1) robustness against imperfect planarity of the calibration target and 2) the number of SL projections per pose. In many industrial SL-based 3-D reconstruction systems, the calibration accuracy greatly affects the reconstruction reliability. Our SL calibration system explicitly deals with calibration target’s imperfect planarity and thus outperforms the existing methods in terms of system accuracy. Another advantage of our SL calibration system is single-shot-per-pose, allowing fast recalibration and reducing the decoding error due to slight pattern misalignment in multishot methods. In addition, we release the open-source calibration software with a graphical user interface (GUI), with which calibration and sparse 3-D reconstruction can be easily performed without any further instructions. Moreover, considering the complex calibration environment and setup, we make the camera and projector imaging parameters, such as exposure, brightness, and contrast, adjustable through widgets and preview. Finally, a limitation of our color-coded SL system is its sensitivity to environment lighting and target texture. This problem may be solved by projector photometric compensation , , ,. [ABSTRACT FROM AUTHOR]

Image-based relighting, projector compensation and depth/normal reconstruction are three important tasks of projector-camera systems (ProCams) and spatial augmented reality (SAR). Although they share a similar pipeline of finding projector-camera image mappings, in tradition, they are addressed independently, sometimes with different prerequisites, devices and sampling images. In practice, this may be cumbersome for SAR applications to address them one-by-one. 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. DeProCams explicitly decomposes the projector-camera image mappings into three subprocesses: shading attributes estimation, rough direct light estimation and photorealistic neural rendering. A particular challenge addressed by DeProCams is occlusion, for which we exploit epipolar constraint and propose a novel differentiable projector direct light mask. Thus, it can be learned end-to-end along with the other modules. Afterwards, to improve convergence, we apply photometric and geometric constraints such that the intermediate results are plausible. In our experiments, DeProCams shows clear advantages over previous arts with promising quality and meanwhile being fully differentiable. Moreover, by solving the three tasks in a unified model, DeProCams waives the need for additional optical devices, radiometric calibrations and structured light. [ABSTRACT FROM AUTHOR]

Motivated by neuroscience discoveries during the last few years, many studies consider pulse-coupled neural networks with spike-timing as an essential component in information processing by the brain. There also exists some technical challenges while simulating the networks of artificial spiking neurons. The existing studies use a Hodgkin–Huxley (H–H) model to describe spiking dynamics and neuro-computational properties of each neuron. But they fail to address the effect of specific non-Gaussian noise on an artificial H–H neuron system. This paper aims to analyze how an artificial H–H neuron responds to add different types of noise using an electrical current and subunit noise model. The spiking and bursting behavior of this neuron is also investigated through numerical simulations. In addition, through statistic analysis, the intensity of different kinds of noise distributions is discussed to obtain their relationship with the mean firing rate, interspike intervals, and stochastic resonance. [ABSTRACT FROM PUBLISHER]