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

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

Impact of Training Images on Radiometric Compensation

The increasing availability of both high-resolution projectors andimperfect displays make radiometric correction an essential componentin all modern projection systems. Particularly, projectingin casual locations, such as classrooms, open areas and homes,calls for the development of radiometric correction techniques thatare fully automatic and deal with display imperfections in real-time.This paper reviews the current radiometric compensation algorithmsand discusses the influence of different training images on theirperformance

Projector Compensation for Unconventional Projection Surface

Projecting onto irregular textured surfaces found on buildings, automobiles and theatre stages calls for the development of radiometric and geometric compensation algorithms that require no user intervention and compensate for the patterning and colourization of the background surface. This process needs a projector-camera setup where the feedback from the camera is used to learn the background's geometric and radiometric properties. In this thesis, radiometric compensation, which is used to correct for the background texture distortion, is discussed in detail. Existing compensation frameworks assume no inter--pixel coupling and develop an independent compensation model for each projector pixel. This assumption is valid on background with uniform texture variation but fails at sharp contrast differences leading to visible edge artifacts in the compensated image. To overcome the edge artifacts, a novel radiometric compensation approach is presented that directly learns the compensation model, rather than inverting a learned forward model. That is, the proposed method uses spatially uniform camera images to learn the projector images that successfully hide the background. The proposed approach can be used with any existing radiometric compensation algorithm to improve its performance. Comparisons with classical and state-of-the-art methods show the superiority of the proposed method in terms of the perceived image quality and computational complexity. The modified target image from the radiometric compensation algorithm can exceed the limited dynamic range of the projector resulting in saturation artifacts in the compensated image. Since the achievable range of luminance on the background surface with the given projector is limited, the projector compensation should also consider the contents of the target image along with the background properties while calculating the projector image. A novel spatially optimized luminance modification approach is proposed using human visual system properties to reduce the saturation artifacts. Here, the tolerance of the human visual system is exploited to make perceptually less sensitive modifications to the target image that in turn reduces the luminance demands from the projector. The proposed spatial modification approach can be combined with any radiometric compensation models to improve its performance. The simulated results of the proposed luminance modification are evaluated to show the improvement in perceptual performance. The inverse approach combined with the spatial luminance modification concludes the proposed projector compensation, which enables the optimum compensated projection on an arbitrary background surface