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

Study of reinforced concrete building demolition methods and code requirements

The life cycle of reinforced concrete buildings is usually 40 to 90 years. However, during this life cycle, buildings will often meet some circumstances, such as disasters, changing functions, city reconstruction, or higher demand for the residence etc.; all of these circumstances will lead to demolition or reconstruction of buildings. Moreover, many of Reinforced Concrete (RC) buildings in Asia or Europe built after World War II have been used for about 50 years or more, and now need to be rebuilt or demolished. But the current demolition methods are not safe enough, and the traditional building demolition project managing methods cannot satisfy the developments of the construction industry in future. So it is quite necessary to consummate the old building demolition regulations and working code, and also to develop more practical building demolition methods.;The research program begins from the collection of related documents, including pertinent building demolition laws, codes, working methods, project examination forms, and regulations in Europe, America, Australia, Mainland China, Japan, Hong Kong, and Taiwan. On the basis of this job, the research compares the building demolition laws and regulations between different countries, and then summarizes the building demolition methods and procedures, in order to put forward the building demolition ordinances amending opinions and compose the Common Reinforced Concrete Building Demolition Code draft for the execution of building demolition projects in Taiwan

Cyclic prefix assisted block transmission for low complexity communication system design

This thesis presents new results on cyclic prefix (CP) assisted block transmission for low complexity communication system design. Two important aspects are studied: the CP based low-complexity schemes for channel equalization and channel estimation. Specifically, based on the simple frequency domain equalization, a low complexity joint receiver is proposed for CP-CDMA system, which is a special application of block transmission. And in this work the finite impulse response (FIR) model is used for the unknown communication channels. To identify an unknown FIR channel, a novel channel estimation method is proposed by exploiting the cyclic prefix technique. Compared to a conventional method, the proposed method delivers the similar estimation accuracy, yet at much lower system overhead and lower computational complexity. In order to minimize the channel total mean square error in channel estimation, the criteria and solutions to optimal training sequence design are also presented. Finally, the performance study is carried out on the proposed channel estimation scheme for BPSK block transmission system as well as CP-CDMA system using simulation along with analysis

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

High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the local geometry refinement network takes as input lo-cal 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and local geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.Comment: 8 pages paper, 11 pages supplementary material, ICCV spotlight pape