2021 IEEE International Conference on Computational Photography (ICCP)最新文献

Depth from Defocus with Learned Optics for Imaging and Occlusion-aware Depth Estimation 离焦深度与学习光学成像和闭塞感知深度估计

2021 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2021-05-23 DOI: 10.1109/ICCP51581.2021.9466261

H. Ikoma, Cindy M. Nguyen, Christopher A. Metzler, Yifan Peng, Gordon Wetzstein

引用次数: 31

Reference Wave Design for Wavefront Sensing 波前传感的参考波设计

2021 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2021-05-23 DOI: 10.1109/ICCP51581.2021.9466263

Wei-yu Chen, Anat Levin, Matthew O'Toole, Aswin C. Sankaranarayanan

引用次数: 1

Depth from Defocus as a Special Case of the Transport of Intensity Equation 离焦深度作为强度输运方程的一个特例

2021 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2021-05-23 DOI: 10.1109/ICCP51581.2021.9466260

Emma Alexander, Leyla A. Kabuli, O. Cossairt, L. Waller

引用次数: 2

Real-Time Light Field 3D Microscopy via Sparsity-Driven Learned Deconvolution 基于稀疏驱动的学习反卷积的实时光场三维显微镜

2021 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2021-05-23 DOI: 10.1109/ICCP51581.2021.9466256

Josué Page Vizcaíno, Zeguan Wang, Panagiotis Symvoulidis, P. Favaro, Burcu Guner-Ataman, E. Boyden, Tobias Lasser

{"title":"Real-Time Light Field 3D Microscopy via Sparsity-Driven Learned Deconvolution","authors":"Josué Page Vizcaíno, Zeguan Wang, Panagiotis Symvoulidis, P. Favaro, Burcu Guner-Ataman, E. Boyden, Tobias Lasser","doi":"10.1109/ICCP51581.2021.9466256","DOIUrl":"https://doi.org/10.1109/ICCP51581.2021.9466256","url":null,"abstract":"Light Field Microscopy (LFM) is a scan-less 3D imaging technique capable of capturing fast biological processes, such as neural activity in zebrafish. However, current methods to recover a 3D volume from the raw data require long reconstruction times hampering the usability of the microscope in a closed-loop system. Moreover, because the main focus of zebrafish brain imaging is to isolate and study neural activity, the ideal volumetric reconstruction should be sparse to reveal the dominant signals. Unfortunately, current sparse decomposition methods are computationally intensive and thus introduce substantial delays. This motivates us to introduce a 3D reconstruction method that recovers the spatio-temporally sparse components of an image sequence in real-time. In this work we propose a combination of a neural network (SLNet) that recovers the sparse components of a light field image sequence and a neural network (XLFMNet) for 3D reconstruction. In particular, XLFMNet is able to achieve high data fidelity and to preserve important signals, such as neural potentials, even on previously unobserved samples. We demonstrate successful sparse 3D volumetric reconstructions of the neural activity of live zebrafish, with an imaging span covering 800×800×250Mm3 at an imaging rate of 24 – 88Hz, which provides a 1500 fold speed increase against prior work and enables real-time reconstructions without sacrificing imaging resolution.","PeriodicalId":132124,"journal":{"name":"2021 IEEE International Conference on Computational Photography (ICCP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121364579","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: 8

Deconvolving Diffraction for Fast Imaging of Sparse Scenes 稀疏场景快速成像的反卷积衍射

2021 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2021-05-23 DOI: 10.1109/ICCP51581.2021.9466266

Mark Sheinin, Matthew O'Toole, S. Narasimhan

{"title":"Deconvolving Diffraction for Fast Imaging of Sparse Scenes","authors":"Mark Sheinin, Matthew O'Toole, S. Narasimhan","doi":"10.1109/ICCP51581.2021.9466266","DOIUrl":"https://doi.org/10.1109/ICCP51581.2021.9466266","url":null,"abstract":"Most computer vision techniques rely on cameras which uniformly sample the 2D image plane. However, there exists a class of applications for which the standard uniform 2D sampling of the image plane is sub-optimal. This class consists of applications where the scene points of interest occupy the image plane sparsely (e.g., marker-based motion capture), and thus most pixels of the 2D camera sensor would be wasted. Recently, diffractive optics were used in conjunction with sparse (e.g., line) sensors to achieve high-speed capture of such sparse scenes. One such approach, called “Diffraction Line Imaging”, relies on the use of diffraction gratings to spread the point-spread-function (PSF) of scene points from a point to a color-coded shape (e.g., a horizontal line) whose intersection with a line sensor enables point positioning. In this paper, we extend this approach for arbitrary diffractive optical elements and arbitrary sampling of the sensor plane using a convolution-based image formation model. Sparse scenes are then recovered by formulating a convolutional coding inverse problem that can resolve mixtures of diffraction PSFs without the use of multiple sensors, extending the application of diffraction-based imaging to a new class of significantly denser scenes. For the case of a single-axis diffraction grating, we provide an approach to determine the minimal required sensor sub-sampling for accurate scene recovery. Compared to methods that use a speckle PSF from a narrow-band source or a diffuser-based PSF with a rolling shutter sensor, our approach uses spectrally-coded PSFs from broad-band sources and allows arbitrary sensor sampling, respectively. We demonstrate that the presented combination of the imaging approach and scene recovery method is well suited for high-speed marker based motion capture and particle image velocimetry (PIV) over long periods.","PeriodicalId":132124,"journal":{"name":"2021 IEEE International Conference on Computational Photography (ICCP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116777480","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: 2

View-dependent Scene Appearance Synthesis using Inverse Rendering from Light Fields 使用光场的反向渲染来合成依赖于视图的场景外观

2021 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2021-05-23 DOI: 10.1109/ICCP51581.2021.9466274

Dahyun Kang, D. S. Jeon, Hak-Il Kim, Hyeonjoong Jang, Min H. Kim

{"title":"View-dependent Scene Appearance Synthesis using Inverse Rendering from Light Fields","authors":"Dahyun Kang, D. S. Jeon, Hak-Il Kim, Hyeonjoong Jang, Min H. Kim","doi":"10.1109/ICCP51581.2021.9466274","DOIUrl":"https://doi.org/10.1109/ICCP51581.2021.9466274","url":null,"abstract":"In order to enable view-dependent appearance synthesis from the light fields of a scene, it is critical to evaluate the geometric relationships between light and view over surfaces in the scene with high accuracy. Perfect diffuse reflectance is commonly assumed to estimate geometry from light fields via multiview stereo. However, this diffuse surface assumption is invalid with real-world objects. Geometry estimated from light fields is severely degraded over specular surfaces. Additional scene-scale 3D scanning based on active illumination could provide reliable geometry, but it is sparse and thus still insufficient to calculate view-dependent appearance, such as specular reflection, in geometry-based view synthesis. In this work, we present a practical solution of inverse rendering to enable view-dependent appearance synthesis, particularly of scene scale. We enhance the scene geometry by eliminating the specular component, thus enforcing photometric consistency. We then estimate spatially-varying parameters of diffuse, specular, and normal components from wide-baseline light fields. To validate our method, we built a wide-baseline light field imaging prototype that consists of 32 machine vision cameras with fisheye lenses of 185 degrees that cover the forward hemispherical appearance of scenes. We captured various indoor scenes, and results validate that our method can estimate scene geometry and reflectance parameters with high accuracy, enabling view-dependent appearance synthesis at scene scale with high fidelity, i.e., specular reflection changes according to a virtual viewpoint.","PeriodicalId":132124,"journal":{"name":"2021 IEEE International Conference on Computational Photography (ICCP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129110218","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: 2

Fast Computational Periscopy in Challenging Ambient Light Conditions through Optimized Preconditioning 通过优化预处理，在具有挑战性的环境光条件下快速计算潜望镜

2021 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2021-05-23 DOI: 10.1109/ICCP51581.2021.9466264

Charles Saunders, V. Goyal

引用次数: 1

Single scattering modeling of speckle correlation 散斑相关的单散射建模

2021 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2021-05-23 DOI: 10.1109/ICCP51581.2021.9466262

Chen Bar, Marina Alterman, Ioannis Gkioulekas, Anat Levin

{"title":"Single scattering modeling of speckle correlation","authors":"Chen Bar, Marina Alterman, Ioannis Gkioulekas, Anat Levin","doi":"10.1109/ICCP51581.2021.9466262","DOIUrl":"https://doi.org/10.1109/ICCP51581.2021.9466262","url":null,"abstract":"Coherent images of scattering materials, such as biological tissue, typically exhibit high-frequency intensity fluctuations known as speckle. These seemingly noise-like speckle patterns have strong statistical correlation properties that have been successfully utilized by computational imaging systems in different application areas. Unfortunately, these properties are not well-understood, in part due to the difficulty of simulating physically-accurate speckle patterns. In this work, we propose a new model for speckle statistics based on a single scattering approximation, that is, the assumption that all light contributing to speckle correlation has scattered only once. Even though single-scattering models have been used in computer vision and graphics to approximate intensity images due to scattering, such models usually hold only for very optically thin materials, where light indeed does not scatter more than once. In contrast, we show that the single-scattering model for speckle correlation remains accurate for much thicker materials. We evaluate the accuracy of the single-scattering correlation model through exhaustive comparisons against an exact speckle correlation simulator. We additionally demonstrate the model's accuracy through comparisons with real lab measurements. We show, that for many practical application settings, predictions from the single-scattering model are more accurate than those from other approximate models popular in optics, such as the diffusion and Fokker-Planck models. We show how to use the single-scattering model to derive closed-form expressions for speckle correlation, and how these expressions can facilitate the study of statistical speckle properties. In particular, we demonstrate that these expressions provide simple explanations for previously reported speckle properties, and lead to the discovery of new ones. Finally, we discuss potential applications for future computational imaging systems.","PeriodicalId":132124,"journal":{"name":"2021 IEEE International Conference on Computational Photography (ICCP)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116502950","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: 12

DeRenderNet: Intrinsic Image Decomposition of Urban Scenes with Shape-(In)dependent Shading Rendering DeRenderNet:基于形状(In)依赖的阴影渲染的城市场景的内在图像分解

2021 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2021-04-28 DOI: 10.1109/ICCP51581.2021.9466269

Yongjie Zhu, Jiajun Tang, Si Li, Boxin Shi

引用次数: 6

Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance 光谱MVIR:三维形状和光谱反射率的联合重建

2021 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2021-04-15 DOI: 10.1109/ICCP51581.2021.9466267

Chunyu Li, Yusuke Monno, M. Okutomi

{"title":"Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance","authors":"Chunyu Li, Yusuke Monno, M. Okutomi","doi":"10.1109/ICCP51581.2021.9466267","DOIUrl":"https://doi.org/10.1109/ICCP51581.2021.9466267","url":null,"abstract":"Reconstructing an object's high-quality 3D shape with inherent spectral reflectance property, beyond typical device-dependent RGB albedos, opens the door to applications requiring a high-fidelity 3D model in terms of both geometry and photometry. In this paper, we propose a novel Multi-View Inverse Rendering (MVIR) method called Spectral MVIR for jointly reconstructing the 3D shape and the spectral reflectance for each point of object surfaces from multi-view images captured using a standard RGB camera and low-cost lighting equipment such as an LED bulb or an LED projector. Our main contributions are twofold: (i) We present a rendering model that considers both geometric and photometric principles in the image formation by explicitly considering camera spectral sensitivity, light's spectral power distribution, and light source positions. (ii) Based on the derived model, we build a cost-optimization MVIR framework for the joint reconstruction of the 3D shape and the per-vertex spectral reflectance while estimating the light source positions and the shadows. Different from most existing spectral-3D acquisition methods, our method does not require expensive special equipment and cumbersome geometric calibration. Experimental results using both synthetic and real-world data demonstrate that our Spectral MVIR can acquire a high-quality 3D model with accurate spectral reflectance property.","PeriodicalId":132124,"journal":{"name":"2021 IEEE International Conference on Computational Photography (ICCP)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122535467","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: 9