483 research outputs found
End-to-end Projector Photometric Compensation
Projector photometric compensation aims to modify a projector input image
such that it can compensate for disturbance from the appearance of projection
surface. In this paper, for the first time, we formulate the compensation
problem as an end-to-end learning problem and propose a convolutional neural
network, named CompenNet, to implicitly learn the complex compensation
function. CompenNet consists of a UNet-like backbone network and an autoencoder
subnet. Such architecture encourages rich multi-level interactions between the
camera-captured projection surface image and the input image, and thus captures
both photometric and environment information of the projection surface. In
addition, the visual details and interaction information are carried to deeper
layers along the multi-level skip convolution layers. The architecture is of
particular importance for the projector compensation task, for which only a
small training dataset is allowed in practice. Another contribution we make is
a novel evaluation benchmark, which is independent of system setup and thus
quantitatively verifiable. 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. Our key idea, motivated
from our end-to-end problem formulation, is to use a reasonable surrogate to
avoid such projection process so as to be setup-independent. Our method is
evaluated carefully on the benchmark, and the results show that our end-to-end
learning solution outperforms state-of-the-arts both qualitatively and
quantitatively by a significant margin.Comment: To appear in the 2019 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). Source code and dataset are available at
https://github.com/BingyaoHuang/compenne
CompenNet++: End-to-end Full Projector Compensation
Full projector compensation aims to modify a projector input image such that
it can compensate for both geometric and photometric disturbance of the
projection surface. Traditional methods usually solve the two parts separately,
although they are known to correlate with each other. In this paper, we propose
the first end-to-end solution, named CompenNet++, to solve the two problems
jointly. Our work non-trivially extends CompenNet, which was recently proposed
for photometric compensation with promising performance. First, we propose a
novel geometric correction subnet, which is designed with a cascaded
coarse-to-fine structure to learn the sampling grid directly from photometric
sampling images. Second, by concatenating the geometric correction subset with
CompenNet, CompenNet++ accomplishes full projector compensation and is
end-to-end trainable. Third, after training, we significantly simplify both
geometric and photometric compensation parts, and hence largely improves the
running time efficiency. Moreover, we construct the first setup-independent
full compensation benchmark to facilitate the study on this topic. In our
thorough experiments, our method shows clear advantages over previous arts with
promising compensation quality and meanwhile being practically convenient.Comment: To appear in ICCV 2019. High-res supplementary material:
https://www3.cs.stonybrook.edu/~hling/publication/CompenNet++_sup-high-res.pdf.
Code: https://github.com/BingyaoHuang/CompenNet-plusplu
Raduga experiment: Multizonal photographing the Earth from the Soyuz-22 spacecraft
The main results of the scientific research and 'Raduga' experiment are reported. Technical parameters are presented for the MKF-6 camera and the MSP-4 projector. Characteristics of the obtained materials and certain results of their processing are reported
Optical-inertia space sextant for an advanced space navigation system, phase B
Optical-inertia space sextant for advanced space navigation syste
Fast Radiometric Compensation for Nonlinear Projectors
Radiometric compensation can be accomplished on nonlinearprojector-camera systems through the use of pixelwise lookup ta-bles. Existing methods are both computationally and memory inten-sive. Such methods are impractical to be implemented for currenthigh-end projector technology. In this paper, a novel computation-ally efficient method for nonlinear radiometric compensation of pro-jectors is proposed. The compensation accuracy of the proposedmethod is assessed with the use of a spectroradiometer. Experi-mental results show both the effectiveness of the method and thereduction in compensation time compared to a recent state-of-the-art method
A Multi-Projector Calibration Method for Virtual Reality Simulators with Analytically Defined Screens
The geometric calibration of projectors is a demanding task, particularly for the industry of virtual reality simulators. Different methods have been developed during the last decades to retrieve the intrinsic and extrinsic parameters of projectors, most of them being based on planar homographies and some requiring an extended calibration process. The aim of our research work is to design a fast and user-friendly method to provide multi-projector calibration on analytically defined screens, where a sample is shown for a virtual reality Formula 1 simulator that has a cylindrical screen. The proposed method results from the combination of surveying, photogrammetry and image processing approaches, and has been designed by considering the spatial restrictions of virtual reality simulators. The method has been validated from a mathematical point of view, and the complete system which is currently installed in a shopping mall in Spain has been tested by different users
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