2018 IEEE International Conference on Computational Photography (ICCP)最新文献

Rolling shutter imaging on the electric grid 电网上的卷帘门成像

2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-04 DOI: 10.1109/ICCPHOT.2018.8368472

Mark Sheinin, Y. Schechner, Kiriakos N. Kutulakos

引用次数: 14

Dynamic heterodyne interferometry 动态外差干涉测量

2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-04 DOI: 10.1109/ICCPHOT.2018.8368471

Tomohiro Maeda, A. Kadambi, Y. Schechner, R. Raskar

引用次数: 16

Learning to see through reflections 学会看透反思

2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-04 DOI: 10.1109/ICCPHOT.2018.8368464

Meiguang Jin, S. Süsstrunk, P. Favaro

引用次数: 14

Acquiring and characterizing plane-to-ray indirect light transport 获取和表征平面到射线的间接光输运

2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-04 DOI: 10.1109/ICCPHOT.2018.8368461

Hiroyuki Kubo, Suren Jayasuriya, Takafumi Iwaguchi, Takuya Funatomi, Y. Mukaigawa, S. Narasimhan

引用次数: 18

Focal sweep imaging with multi-focal diffractive optics 多焦衍射光学的焦扫描成像

2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-04 DOI: 10.1109/ICCPHOT.2018.8368469

Yifan Peng, Xiong Dun, Qilin Sun, Felix Heide, W. Heidrich

引用次数: 6

Towards photography through realistic fog 通过逼真的雾来摄影

2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-04 DOI: 10.1109/ICCPHOT.2018.8368463

Guy Satat, Matthew Tancik, R. Raskar

{"title":"Towards photography through realistic fog","authors":"Guy Satat, Matthew Tancik, R. Raskar","doi":"10.1109/ICCPHOT.2018.8368463","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2018.8368463","url":null,"abstract":"Imaging through fog has important applications in industries such as self-driving cars, augmented driving, airplanes, helicopters, drones and trains. Here we show that time profiles of light reflected from fog have a distribution (Gamma) that is different from light reflected from objects occluded by fog (Gaussian). This helps to distinguish between background photons reflected from the fog and signal photons reflected from the occluded object. Based on this observation, we recover reflectance and depth of a scene obstructed by dense, dynamic, and heterogeneous fog. For practical use cases, the imaging system is designed in optical reflection mode with minimal footprint and is based on LIDAR hardware. Specifically, we use a single photon avalanche diode (SPAD) camera that time-tags individual detected photons. A probabilistic computational framework is developed to estimate the fog properties from the measurement itself, without prior knowledge. Other solutions are based on radar that suffers from poor resolution (due to the long wavelength), or on time gating that suffers from low signal-to-noise ratio. The suggested technique is experimentally evaluated in a wide range of fog densities created in a fog chamber It demonstrates recovering objects 57cm away from the camera when the visibility is 37cm. In that case it recovers depth with a resolution of 5cm and scene reflectance with an improvement of 4dB in PSNR and 3.4x reconstruction quality in SSIM over time gating techniques.","PeriodicalId":413914,"journal":{"name":"2018 IEEE International Conference on Computational Photography (ICCP)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121947475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 79

Towards transient imaging at interactive rates with single-photon detectors 与单光子探测器交互速率的瞬态成像研究

2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-04 DOI: 10.1109/ICCPHOT.2018.8368466

David B. Lindell, Matthew O'Toole, Gordon Wetzstein

引用次数: 19

Reconfigurable rainbow PIV for 3D flow measurement 可重构的彩虹PIV三维流量测量

2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-04 DOI: 10.1109/ICCPHOT.2018.8368475

J. Xiong, Q. Fu, R. Idoughi, W. Heidrich

引用次数: 16

Near-light photometric stereo using circularly placed point light sources 使用圆形放置的点光源的近光立体测光

2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-04 DOI: 10.1109/ICCPHOT.2018.8368465

Chao Liu, S. Narasimhan, A. Dubrawski

{"title":"Near-light photometric stereo using circularly placed point light sources","authors":"Chao Liu, S. Narasimhan, A. Dubrawski","doi":"10.1109/ICCPHOT.2018.8368465","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2018.8368465","url":null,"abstract":"Most photometric stereo approaches assume distant or directional lighting and orthographic imaging. However, when the source is divergent and is near the object and the camera is projective, the image intensity of a Lambertian object is a non-linear function of both the unknown surface normals and the unknown distances of the source to the surface points. The resulting non-linear optimization is non-convex and highly sensitive to the initial guess. In this paper, we propose a two-stage near-light photometric stereo method using circularly placed point light sources (commonly seen in recent consumer imaging devices like NESTcam, Amazon Cloudcam, etc). We represent the scene using a 3D mesh and directly optimize the vertices of the mesh. This reduces the complexity of the relationship between surface normals and depths in the image formation model. In the first stage, we optimize the vertex positions using the differential images induced by small changes in light source position. This procedure yields a strong initial guess for the second stage that refines the estimations using the raw captured images. We propose an accurate calibration approach to estimate the positions of the sources. Our approach performs better on simulations and on real Lambertian scenes with complex shapes than the state-of-the-art method with near-field lighting.","PeriodicalId":413914,"journal":{"name":"2018 IEEE International Conference on Computational Photography (ICCP)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126178894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Deep learning for the design of nano-photonic structures 纳米光子结构的深度学习设计

2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-04 DOI: 10.1109/ICCPHOT.2018.8368462

Itzik Malkiel, M. Mrejen, Achiya Nagler, U. Arieli, Lior Wolf, H. Suchowski

{"title":"Deep learning for the design of nano-photonic structures","authors":"Itzik Malkiel, M. Mrejen, Achiya Nagler, U. Arieli, Lior Wolf, H. Suchowski","doi":"10.1109/ICCPHOT.2018.8368462","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2018.8368462","url":null,"abstract":"Our visual perception of our surroundings is ultimately limited by the diffraction-limit, which stipulates that optical information smaller than roughly half the illumination wavelength is not retrievable. Over the past decades, many breakthroughs have led to unprecedented imaging capabilities beyond the diffraction-limit, with applications in biology and nanotechnology. In this context, nano-photonics has had a profound impact on the field of optics by enabling the manipulation of light-matter interaction with subwave-length structures [1, 2, 3]. However, despite the many advances in this field, its impact and penetration in our daily life has been hindered by a convoluted and iterative process, cycling through modeling, nanofabrication and nano-characterization. The fundamental reason is the fact that not only the prediction of the optical response is very time consuming and requires solving Maxwell's equations with dedicated numerical packages [4, 5, 6]. But, more significantly, the inverse problem, i.e. designing a nanostructure with an on-demand optical response, is currently a prohibitive task even with the most advanced numerical tools due to the high non-linearity of the problem [7, 8]. Here, we harness the power of Deep Learning and show its ability to predict the geometry of nanostructures based solely on their far-field response. This approach addresses in a direct way the currently inaccessible inverse problem breaking the ground for on-demand design of optical response with applications such as sensing, imaging and also for Plasmons mediated cancer thermotherapy.","PeriodicalId":413914,"journal":{"name":"2018 IEEE International Conference on Computational Photography (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129388329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21