Pub Date : 2012-04-28DOI: 10.1109/ICCPhot.2012.6215219
Nitesh Shroff, A. Veeraraghavan, Yuichi Taguchi, Oncel Tuzel, Amit K. Agrawal, R. Chellappa
Traditional depth from defocus (DFD) algorithms assume that the camera and the scene are static during acquisition time. In this paper, we examine the effects of camera and scene motion on DFD algorithms. We show that, given accurate estimates of optical flow (OF), one can robustly warp the focal stack (FS) images to obtain a virtual static FS and apply traditional DFD algorithms on the static FS. Acquiring accurate OF in the presence of varying focal blur is a challenging task. We show how defocus blur variations cause inherent biases in the estimates of optical flow. We then show how to robustly handle these biases and compute accurate OF estimates in the presence of varying focal blur. This leads to an architecture and an algorithm that converts a traditional 30 fps video camera into a co-located 30 fps image and a range sensor. Further, the ability to extract image and range information allows us to render images with artistic depth-of field effects, both extending and reducing the depth of field of the captured images. We demonstrate experimental results on challenging scenes captured using a camera prototype.
{"title":"Variable focus video: Reconstructing depth and video for dynamic scenes","authors":"Nitesh Shroff, A. Veeraraghavan, Yuichi Taguchi, Oncel Tuzel, Amit K. Agrawal, R. Chellappa","doi":"10.1109/ICCPhot.2012.6215219","DOIUrl":"https://doi.org/10.1109/ICCPhot.2012.6215219","url":null,"abstract":"Traditional depth from defocus (DFD) algorithms assume that the camera and the scene are static during acquisition time. In this paper, we examine the effects of camera and scene motion on DFD algorithms. We show that, given accurate estimates of optical flow (OF), one can robustly warp the focal stack (FS) images to obtain a virtual static FS and apply traditional DFD algorithms on the static FS. Acquiring accurate OF in the presence of varying focal blur is a challenging task. We show how defocus blur variations cause inherent biases in the estimates of optical flow. We then show how to robustly handle these biases and compute accurate OF estimates in the presence of varying focal blur. This leads to an architecture and an algorithm that converts a traditional 30 fps video camera into a co-located 30 fps image and a range sensor. Further, the ability to extract image and range information allows us to render images with artistic depth-of field effects, both extending and reducing the depth of field of the captured images. We demonstrate experimental results on challenging scenes captured using a camera prototype.","PeriodicalId":169984,"journal":{"name":"2012 IEEE International Conference on Computational Photography (ICCP)","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129823173","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}
Pub Date : 2012-04-28DOI: 10.1109/ICCPhot.2012.6215210
F. Pérez, Alejandro Pérez, Manuel Rodríguez, E. Magdaleno
Plenoptic cameras are a promising solution to increase the capabilities of current commercial cameras because they capture the four-dimensional lightfield of a scene. Processing the recorded lightfield, these cameras offer the possibility of focusing the scene after the shot or obtaining 3D information. Conventional photographs focused on determined planes can be obtained through projections of the four-dimensional lightfield onto two spatial dimensions. These photographs can be efficiently computed using the Fourier Slice technique but their resolution is usually less than 1% of the full resolution of the camera sensor. Several super-resolution methods have been recently developed to increase the spatial resolution of plenoptic cameras. In this paper we propose a new super-resolution method based on the Fourier Slice technique. We show how several existing super-resolution methods can be seen as particular cases of this approach. Besides the theoretical interest of this unified view, we also show how to obtain simultaneously spatial and depth super-resolution removing the limitations of previous approaches.
{"title":"Fourier Slice Super-resolution in plenoptic cameras","authors":"F. Pérez, Alejandro Pérez, Manuel Rodríguez, E. Magdaleno","doi":"10.1109/ICCPhot.2012.6215210","DOIUrl":"https://doi.org/10.1109/ICCPhot.2012.6215210","url":null,"abstract":"Plenoptic cameras are a promising solution to increase the capabilities of current commercial cameras because they capture the four-dimensional lightfield of a scene. Processing the recorded lightfield, these cameras offer the possibility of focusing the scene after the shot or obtaining 3D information. Conventional photographs focused on determined planes can be obtained through projections of the four-dimensional lightfield onto two spatial dimensions. These photographs can be efficiently computed using the Fourier Slice technique but their resolution is usually less than 1% of the full resolution of the camera sensor. Several super-resolution methods have been recently developed to increase the spatial resolution of plenoptic cameras. In this paper we propose a new super-resolution method based on the Fourier Slice technique. We show how several existing super-resolution methods can be seen as particular cases of this approach. Besides the theoretical interest of this unified view, we also show how to obtain simultaneously spatial and depth super-resolution removing the limitations of previous approaches.","PeriodicalId":169984,"journal":{"name":"2012 IEEE International Conference on Computational Photography (ICCP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134644456","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}
Pub Date : 2012-04-28DOI: 10.1109/ICCPhot.2012.6215213
Matthias Grundmann, Vivek Kwatra, Daniel Castro, Irfan Essa
We present a novel algorithm for efficient removal of rolling shutter distortions in uncalibrated streaming videos. Our proposed method is calibration free as it does not need any knowledge of the camera used, nor does it require calibration using specially recorded calibration sequences. Our algorithm can perform rolling shutter removal under varying focal lengths, as in videos from CMOS cameras equipped with an optical zoom. We evaluate our approach across a broad range of cameras and video sequences demonstrating robustness, scaleability, and repeatability. We also conducted a user study, which demonstrates preference for the output of our algorithm over other state-of-the art methods. Our algorithm is computationally efficient, easy to parallelize, and robust to challenging artifacts introduced by various cameras with differing technologies.
{"title":"Calibration-free rolling shutter removal","authors":"Matthias Grundmann, Vivek Kwatra, Daniel Castro, Irfan Essa","doi":"10.1109/ICCPhot.2012.6215213","DOIUrl":"https://doi.org/10.1109/ICCPhot.2012.6215213","url":null,"abstract":"We present a novel algorithm for efficient removal of rolling shutter distortions in uncalibrated streaming videos. Our proposed method is calibration free as it does not need any knowledge of the camera used, nor does it require calibration using specially recorded calibration sequences. Our algorithm can perform rolling shutter removal under varying focal lengths, as in videos from CMOS cameras equipped with an optical zoom. We evaluate our approach across a broad range of cameras and video sequences demonstrating robustness, scaleability, and repeatability. We also conducted a user study, which demonstrates preference for the output of our algorithm over other state-of-the art methods. Our algorithm is computationally efficient, easy to parallelize, and robust to challenging artifacts introduced by various cameras with differing technologies.","PeriodicalId":169984,"journal":{"name":"2012 IEEE International Conference on Computational Photography (ICCP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129652154","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}
Pub Date : 2012-04-28DOI: 10.1109/ICCPhot.2012.6215211
Jason Holloway, Aswin C. Sankaranarayanan, A. Veeraraghavan, S. Tambe
Video cameras are invariably bandwidth limited and this results in a trade-off between spatial and temporal resolution. Advances in sensor manufacturing technology have tremendously increased the available spatial resolution of modern cameras while simultaneously lowering the costs of these sensors. In stark contrast, hardware improvements in temporal resolution have been modest. One solution to enhance temporal resolution is to use high bandwidth imaging devices such as high speed sensors and camera arrays. Unfortunately, these solutions are expensive. An alternate solution is motivated by recent advances in computational imaging and compressive sensing. Camera designs based on these principles, typically, modulate the incoming video using spatio-temporal light modulators and capture the modulated video at a lower bandwidth. Reconstruction algorithms, motivated by compressive sensing, are subsequently used to recover the high bandwidth video at high fidelity. Though promising, these methods have been limited since they require complex and expensive light modulators that make the techniques difficult to realize in practice. In this paper, we show that a simple coded exposure modulation is sufficient to reconstruct high speed videos. We propose the Flutter Shutter Video Camera (FSVC) in which each exposure of the sensor is temporally coded using an independent pseudo-random sequence. Such exposure coding is easily achieved in modern sensors and is already a feature of several machine vision cameras. We also develop two algorithms for reconstructing the high speed video; the first based on minimizing the total variation of the spatio-temporal slices of the video and the second based on a data driven dictionary based approximation. We perform evaluation on simulated videos and real data to illustrate the robustness of our system.
{"title":"Flutter Shutter Video Camera for compressive sensing of videos","authors":"Jason Holloway, Aswin C. Sankaranarayanan, A. Veeraraghavan, S. Tambe","doi":"10.1109/ICCPhot.2012.6215211","DOIUrl":"https://doi.org/10.1109/ICCPhot.2012.6215211","url":null,"abstract":"Video cameras are invariably bandwidth limited and this results in a trade-off between spatial and temporal resolution. Advances in sensor manufacturing technology have tremendously increased the available spatial resolution of modern cameras while simultaneously lowering the costs of these sensors. In stark contrast, hardware improvements in temporal resolution have been modest. One solution to enhance temporal resolution is to use high bandwidth imaging devices such as high speed sensors and camera arrays. Unfortunately, these solutions are expensive. An alternate solution is motivated by recent advances in computational imaging and compressive sensing. Camera designs based on these principles, typically, modulate the incoming video using spatio-temporal light modulators and capture the modulated video at a lower bandwidth. Reconstruction algorithms, motivated by compressive sensing, are subsequently used to recover the high bandwidth video at high fidelity. Though promising, these methods have been limited since they require complex and expensive light modulators that make the techniques difficult to realize in practice. In this paper, we show that a simple coded exposure modulation is sufficient to reconstruct high speed videos. We propose the Flutter Shutter Video Camera (FSVC) in which each exposure of the sensor is temporally coded using an independent pseudo-random sequence. Such exposure coding is easily achieved in modern sensors and is already a feature of several machine vision cameras. We also develop two algorithms for reconstructing the high speed video; the first based on minimizing the total variation of the spatio-temporal slices of the video and the second based on a data driven dictionary based approximation. We perform evaluation on simulated videos and real data to illustrate the robustness of our system.","PeriodicalId":169984,"journal":{"name":"2012 IEEE International Conference on Computational Photography (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131119810","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}
Pub Date : 2012-04-28DOI: 10.1109/ICCPhot.2012.6215212
Aswin C. Sankaranarayanan, Christoph Studer, Richard Baraniuk
Compressive sensing (CS)-based spatial-multiplexing cameras (SMCs) sample a scene through a series of coded projections using a spatial light modulator and a few optical sensor elements. SMC architectures are particularly useful when imaging at wavelengths for which full-frame sensors are too cumbersome or expensive. While existing recovery algorithms for SMCs perform well for static images, they typically fail for time-varying scenes (videos). In this paper, we propose a novel CS multi-scale video (CS-MUVI) sensing and recovery framework for SMCs. Our framework features a co-designed video CS sensing matrix and recovery algorithm that provide an efficiently computable low-resolution video preview. We estimate the scene's optical flow from the video preview and feed it into a convex-optimization algorithm to recover the high-resolution video. We demonstrate the performance and capabilities of the CS-MUVI framework for different scenes.
{"title":"CS-MUVI: Video compressive sensing for spatial-multiplexing cameras","authors":"Aswin C. Sankaranarayanan, Christoph Studer, Richard Baraniuk","doi":"10.1109/ICCPhot.2012.6215212","DOIUrl":"https://doi.org/10.1109/ICCPhot.2012.6215212","url":null,"abstract":"Compressive sensing (CS)-based spatial-multiplexing cameras (SMCs) sample a scene through a series of coded projections using a spatial light modulator and a few optical sensor elements. SMC architectures are particularly useful when imaging at wavelengths for which full-frame sensors are too cumbersome or expensive. While existing recovery algorithms for SMCs perform well for static images, they typically fail for time-varying scenes (videos). In this paper, we propose a novel CS multi-scale video (CS-MUVI) sensing and recovery framework for SMCs. Our framework features a co-designed video CS sensing matrix and recovery algorithm that provide an efficiently computable low-resolution video preview. We estimate the scene's optical flow from the video preview and feed it into a convex-optimization algorithm to recover the high-resolution video. We demonstrate the performance and capabilities of the CS-MUVI framework for different scenes.","PeriodicalId":169984,"journal":{"name":"2012 IEEE International Conference on Computational Photography (ICCP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128689309","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}
Pub Date : 2012-04-28DOI: 10.1109/ICCPhot.2012.6215215
Cewu Lu, Li Xu, Jiaya Jia
Decolorization - the process to transform a color image to a grayscale one - is a basic tool in digital printing, stylized black-and-white photography, and in many single channel image processing applications. In this paper, we propose an optimization approach aiming at maximally preserving the original color contrast. Our main contribution is to alleviate a strict order constraint for color mapping based on human vision system, which enables the employment of a bimodal distribution to constrain spatial pixel difference and allows for automatic selection of suitable gray scale in order to preserve the original contrast. Both the quantitative and qualitative evaluation bears out the effectiveness of the proposed method.
{"title":"Contrast preserving decolorization","authors":"Cewu Lu, Li Xu, Jiaya Jia","doi":"10.1109/ICCPhot.2012.6215215","DOIUrl":"https://doi.org/10.1109/ICCPhot.2012.6215215","url":null,"abstract":"Decolorization - the process to transform a color image to a grayscale one - is a basic tool in digital printing, stylized black-and-white photography, and in many single channel image processing applications. In this paper, we propose an optimization approach aiming at maximally preserving the original color contrast. Our main contribution is to alleviate a strict order constraint for color mapping based on human vision system, which enables the employment of a bimodal distribution to constrain spatial pixel difference and allows for automatic selection of suitable gray scale in order to preserve the original contrast. Both the quantitative and qualitative evaluation bears out the effectiveness of the proposed method.","PeriodicalId":169984,"journal":{"name":"2012 IEEE International Conference on Computational Photography (ICCP)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115296876","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}
Pub Date : 2012-04-28DOI: 10.1109/ICCPhot.2012.6215217
Raoul de Charette, R. Tamburo, P. Barnum, Anthony G. Rowe, T. Kanade, S. Narasimhan
During low-light conditions, drivers rely mainly on headlights to improve visibility. But in the presence of rain and snow, headlights can paradoxically reduce visibility due to light reflected off of precipitation back towards the driver. Precipitation also scatters light across a wide range of angles that disrupts the vision of drivers in oncoming vehicles. In contrast to recent computer vision methods that digitally remove rain and snow streaks from captured images, we present a system that will directly improve driver visibility by controlling illumination in response to detected precipitation. The motion of precipitation is tracked and only the space around particles is illuminated using fast dynamic control. Using a physics-based simulator, we show how such a system would perform under a variety of weather conditions. We build and evaluate a proof-of-concept system that can avoid water drops generated in the laboratory.
{"title":"Fast reactive control for illumination through rain and snow","authors":"Raoul de Charette, R. Tamburo, P. Barnum, Anthony G. Rowe, T. Kanade, S. Narasimhan","doi":"10.1109/ICCPhot.2012.6215217","DOIUrl":"https://doi.org/10.1109/ICCPhot.2012.6215217","url":null,"abstract":"During low-light conditions, drivers rely mainly on headlights to improve visibility. But in the presence of rain and snow, headlights can paradoxically reduce visibility due to light reflected off of precipitation back towards the driver. Precipitation also scatters light across a wide range of angles that disrupts the vision of drivers in oncoming vehicles. In contrast to recent computer vision methods that digitally remove rain and snow streaks from captured images, we present a system that will directly improve driver visibility by controlling illumination in response to detected precipitation. The motion of precipitation is tracked and only the space around particles is illuminated using fast dynamic control. Using a physics-based simulator, we show how such a system would perform under a variety of weather conditions. We build and evaluate a proof-of-concept system that can avoid water drops generated in the laboratory.","PeriodicalId":169984,"journal":{"name":"2012 IEEE International Conference on Computational Photography (ICCP)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121423065","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}
Pub Date : 2012-04-28DOI: 10.1109/ICCPhot.2012.6215220
Li Xu, Jiaya Jia
Motion deblurring from images that are captured in a scene with depth variation needs to estimate spatially-varying point spread functions (PSFs). We tackle this problemwith a stereopsis configuration, using depth information to help blur removal. We observe that the simple scheme to partition the blurred images into regions and estimate their PSFs respectively may make small-size regions lack necessary structural information to guide PSF estimation and accordingly propose region trees to hierarchically estimate them. Erroneous PSFs are rejected with a novel PSF selection scheme, based on the shock filtering invariance of natural images. Our framework also applies to general single-image spatially-varying deblurring.
{"title":"Depth-aware motion deblurring","authors":"Li Xu, Jiaya Jia","doi":"10.1109/ICCPhot.2012.6215220","DOIUrl":"https://doi.org/10.1109/ICCPhot.2012.6215220","url":null,"abstract":"Motion deblurring from images that are captured in a scene with depth variation needs to estimate spatially-varying point spread functions (PSFs). We tackle this problemwith a stereopsis configuration, using depth information to help blur removal. We observe that the simple scheme to partition the blurred images into regions and estimate their PSFs respectively may make small-size regions lack necessary structural information to guide PSF estimation and accordingly propose region trees to hierarchically estimate them. Erroneous PSFs are rejected with a novel PSF selection scheme, based on the shock filtering invariance of natural images. Our framework also applies to general single-image spatially-varying deblurring.","PeriodicalId":169984,"journal":{"name":"2012 IEEE International Conference on Computational Photography (ICCP)","volume":"650 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133216560","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}
Pub Date : 2012-04-28DOI: 10.1109/ICCPhot.2012.6215218
Martin Lenz, David Ferstl, M. Rüther, H. Bischof
We present a novel shape from focus method for high- speed shape reconstruction in optical microscopy. While the traditional shape from focus approach heavily depends on presence of surface texture, and requires a considerable amount of measurement time, our method is able to perform reconstruction from only two images. Our method relies the rapid projection of a binary pattern sequence, while object is continuously moved through the camera focus range and a single image is continuously exposed. Deconvolution of the integral image allows a direct decoding of binary pattern and its associated depth. Experiments a synthetic dataset and on real scenes show that a depth map can be reconstructed at only 3% of memory costs and fraction of the computational effort compared with traditional shape from focus.
{"title":"Depth coded shape from focus","authors":"Martin Lenz, David Ferstl, M. Rüther, H. Bischof","doi":"10.1109/ICCPhot.2012.6215218","DOIUrl":"https://doi.org/10.1109/ICCPhot.2012.6215218","url":null,"abstract":"We present a novel shape from focus method for high- speed shape reconstruction in optical microscopy. While the traditional shape from focus approach heavily depends on presence of surface texture, and requires a considerable amount of measurement time, our method is able to perform reconstruction from only two images. Our method relies the rapid projection of a binary pattern sequence, while object is continuously moved through the camera focus range and a single image is continuously exposed. Deconvolution of the integral image allows a direct decoding of binary pattern and its associated depth. Experiments a synthetic dataset and on real scenes show that a depth map can be reconstructed at only 3% of memory costs and fraction of the computational effort compared with traditional shape from focus.","PeriodicalId":169984,"journal":{"name":"2012 IEEE International Conference on Computational Photography (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131004020","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}
Pub Date : 2012-04-28DOI: 10.1109/ICCPhot.2012.6215216
S. Nayar, Mohit Gupta
Today, structured light systems are widely used in applications such as robotic assembly, visual inspection, surgery, entertainment, games and digitization of cultural heritage. Current structured light methods are faced with two serious limitations. First, they are unable to cope with scene regions that produce strong highlights due to specular reflection. Second, they cannot recover useful information for regions that lie within shadows. We observe that many structured light methods use illumination patterns that have translational symmetry, i.e., two-dimensional patterns that vary only along one of the two dimensions. We show that, for this class of patterns, diffusion of the patterns along the axis of translation can mitigate the adverse effects of specularities and shadows. We show results for two applications - 3D scanning using phase shifting of sinusoidal patterns and separation of direct and global components of light transport using high-frequency binary stripes.
{"title":"Diffuse structured light","authors":"S. Nayar, Mohit Gupta","doi":"10.1109/ICCPhot.2012.6215216","DOIUrl":"https://doi.org/10.1109/ICCPhot.2012.6215216","url":null,"abstract":"Today, structured light systems are widely used in applications such as robotic assembly, visual inspection, surgery, entertainment, games and digitization of cultural heritage. Current structured light methods are faced with two serious limitations. First, they are unable to cope with scene regions that produce strong highlights due to specular reflection. Second, they cannot recover useful information for regions that lie within shadows. We observe that many structured light methods use illumination patterns that have translational symmetry, i.e., two-dimensional patterns that vary only along one of the two dimensions. We show that, for this class of patterns, diffusion of the patterns along the axis of translation can mitigate the adverse effects of specularities and shadows. We show results for two applications - 3D scanning using phase shifting of sinusoidal patterns and separation of direct and global components of light transport using high-frequency binary stripes.","PeriodicalId":169984,"journal":{"name":"2012 IEEE International Conference on Computational Photography (ICCP)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122093225","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：