{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/end-to-end-projector-photometric-compensation","title":"End-to-end Projector Photometric Compensation","arxiv_id":"1904.04335","date":"2019-04-08","proceeding":"CVPR 2019 6","authors":["Bingyao Huang","Haibin Ling"],"abstract":"Projector photometric compensation aims to modify a projector input image\nsuch that it can compensate for disturbance from the appearance of projection\nsurface. In this paper, for the first time, we formulate the compensation\nproblem as an end-to-end learning problem and propose a convolutional neural\nnetwork, named CompenNet, to implicitly learn the complex compensation\nfunction. CompenNet consists of a UNet-like backbone network and an autoencoder\nsubnet. Such architecture encourages rich multi-level interactions between the\ncamera-captured projection surface image and the input image, and thus captures\nboth photometric and environment information of the projection surface. In\naddition, the visual details and interaction information are carried to deeper\nlayers along the multi-level skip convolution layers. The architecture is of\nparticular importance for the projector compensation task, for which only a\nsmall training dataset is allowed in practice. Another contribution we make is\na novel evaluation benchmark, which is independent of system setup and thus\nquantitatively verifiable. Such benchmark is not previously available, to our\nbest knowledge, due to the fact that conventional evaluation requests the\nhardware system to actually project the final results. Our key idea, motivated\nfrom our end-to-end problem formulation, is to use a reasonable surrogate to\navoid such projection process so as to be setup-independent. Our method is\nevaluated carefully on the benchmark, and the results show that our end-to-end\nlearning solution outperforms state-of-the-arts both qualitatively and\nquantitatively by a significant margin.","url_abs":"http://arxiv.org/abs/1904.04335v1","url_pdf":"http://arxiv.org/pdf/1904.04335v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"end-to-end-projector-photometric-compensation","repo_url":"https://github.com/BingyaoHuang/CompenNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}