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IEEE Workshop on Applications of Computer vision (WACv)

作者： Rakesh Raj Madavan Akshat Kaimal Badhrinarayanan K v vinayak Gupta Rohit Choudhary Chandrakala Shanmuganathan Kaushik Mitra Shiv Nadar University Chennai Indian Institute of Technology Madras

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Lensless imaging offers a significant opportunity to develop ultra-compact cameras by removing the conventional bulky lens system. However, without a focusing element, the sensor's output is no longer a direct image but a complex multiplexed scene representation. Traditional methods have attempted to address this challenge by employing learnable inversions and refinement models, but these methods are primarily designed for 2D reconstruction and do not generalize well to 3D reconstruction. We introduce GANESH, a novel framework designed to enable simultaneous refinement and novel view synthesis from multi-view lensless images. Unlike existing methods that require scene-specific training, our approach supports on-the-fiy inference without retraining on each scene. Moreover, our framework allows us to tune our model to specific scenes, enhancing the rendering and refinement quality. To facilitate research in this area, we also present the first multi-view lensless dataset, LenslessScenes. Extensive experiments demonstrate that our method outperforms current approaches in reconstruction accuracy and refinement quality. Code and video results are available here.

关键词： Training Solid modeling Three-dimensional displays virtual reality Rendering (computer graphics) Neural radiance field Robustness Wearable devices image reconstruction Synthetic data

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image reconstruction from incomplete data v

Image Reconstruction from Incomplete Data V

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image reconstruction from incomplete data v

ISBN: (纸本)9780819472960

The proceedings contain 21 papers. The topics discussed include: spectral image estimation for coded aperture snapshot spectral imagers;exploiting image sparsity in parallel magnetic resonance imaging (pMRI);spatio-temporal sampling for video;multi-spectral intensity diffraction tomography;inversion of strongly scattered data: shape and permittivity recovery;evaluation of surrogate data quality in sinogram-based CT metal-artifact reduction;an EM-like optimization scheme for diffuse optical tomography;advances in ptychographical coherent diffractive imaging;image reconstruction by phase retrieval with transverse translation diversity;turbulence profiling for long range surveillance imaging;superresolution in dewraped in anisoplanatic images;and self-tuning Kalman filter estimation at atmospheric wrap.

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Seeing the Unseen in Micro-video Popularity Prediction: Self-Correlation Retrieval for Missing Modality Generation 25

Seeing the Unseen in Micro-Video Popularity Prediction: Self...

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Proceedings of the 31st ACM SIGKDD conference on Knowledge Discovery and data Mining v.1

作者： Zhangtao Cheng Jian Lang Ting Zhong Fan Zhou University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (纸本)9798400712456

Micro-video popularity prediction (MvPP) plays a crucial role in numerous real-world applications, including product marketing and recommendation systems. While existing methodologies predominantly assume complete modalities during multimodal learning, this assumption often fails to hold in practical scenarios due to various constraints, such as privacy concerns or data integrity issues. To address this limitation, we propose SCRAG, a novel Self-Correlation Retrieval-Augmented Generative framework designed to enhance missing-modality robustness in MvPP. SCRAG operates in a retrieval-guided generation manner that explores relevant knowledge to enhance the reconstruction of missing content, which consists of two primary components: (1) a self-correlation retriever and (2) a multimodal mixture-of-experts generator. It first acquires instances pertinent to the missing content through multimodal prompt alignment. Subsequently, the generator extracts contextual modal information from the retrieved context-rich instances. By learning the joint distribution of modalities, SCRAG effectively recovers missing content and addresses the modal heterogeneity challenge inherent in cross-modal generation approaches. Extensive experiments conducted on three real-world datasets demonstrate that SCRAG consistently outperforms state-of-the-art baselines, underscoring its effectiveness in handling incomplete modalities and improving the accuracy of micro-video popularity prediction.

关键词： incomplete multimodal learning

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PGRID: Power Grid reconstruction in Informal Developments Using High-Resolution Aerial imagery

PGRID: Power Grid Reconstruction in Informal Developments Us...

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IEEE Workshop on Applications of Computer vision (WACv)

作者： Simone Fobi Nsutezo Amrita Gupta Duncan Kebut Seema Iyer Luana Marotti Rahul Dodhia Juan M. Lavista Ferres Anthony Ortiz Microsoft AI for Good Research Lab HOTOSM USA for UNHCR

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

As of 2023, a record 117 million people have been dis-placed worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR's mandate, with 8.7 million residing in refugee camps. A critical issue faced by these populations is the lack of access to electricity, with 80% of the 8.7 million refugees and displaced persons in camps globally relying on traditional biomass for cooking and lacking reliable power for essential tasks such as cooking and charging phones. Often, the burden of collecting firewood falls on women and children, who frequently travel up to 20 kilometers into dan-gerous areas, increasing their vulnerability. [7] Electricity access could significantly alleviate these challenges, but a major obstacle is the lack of accurate power grid infrastructure maps, particularly in resource-constrained environments like refugee camps, needed for energy access planning. Existing power grid maps are often outdated, incomplete, or dependent on costly, complex technologies, limiting their practicality. To address this issue, PGRID is a novel application-based approach, which utilizes high-resolution aerial imagery to detect electrical poles and segment electrical lines, creating precise power grid maps. PGRID was tested in the Turkana region of Kenya, specifically the Kakuma and Kalobeyei Camps, cov-ering 84 km 2 and housing over 200,000 residents. Our findings show that PGRID delivers high-fidelity power grid maps especially in unplanned settlements, with F1-scores of 0.71 and 0.82 for pole detection and line segmentation, respectively. This study highlights a practical application for leveraging open data and limited labels to improve power grid mapping in unplanned settlements, where the growing number of displaced persons urgently need sustainable energy infrastructure solutions.

关键词： image segmentation Accuracy Electricity Source coding Power grids Planning Resource management Reliability Open data image reconstruction

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Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow*

Multi-modal Dynamic Point Cloud Geometric Compression Based ...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fangzhe Nan Frederick Li Zhuoyue Wang Gary K.L. Tam Zhaoyi Jiang DongZheng DongZheng Bailin Yang School of Statistics and Mathematics Zhejiang Gongshang University School of Computer Science and Technolgy Zhejiang Gongshang University University of Durham University of California Berkeley Swansea University Universal Ubiquitous Technology Co. LTD

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning methods have recently shown significant promise in compressing the geometric features of point clouds. However, challenges arise when consecutive point clouds contain holes, resulting in incomplete information that complicates motion estimation. To our knowledge, most existing dynamic point cloud compression methods have largely overlooked this critical issue. Moreover, these methods typically employ a multi-scale single-pass approach for motion estimation, performing only one estimation at each scale. This limits accuracy and adversely impacts compression performance. To address these challenges, we propose a dynamic point cloud compression model called M2BR-DPCC (Multi-Modal Multi-Scale Bidirectional Recursion for Dynamic Point Cloud Compression). Our method introduces two key innovations. First, we integrate both point cloud and image data as inputs, leveraging a multi-modal feature representation completion (MFRepC) approach to align information across modalities. This addresses the issue of missing data in point clouds by using complementary information from images. Second, we implement a multi-scale bidirectional recursive (MSBR) motion estimation method. This module iteratively refines motion flows in both forward and backward directions, progressively enhancing point cloud features and improving motion estimation accuracy. Experimental results on widely used datasets, including MvUB and 8ivFB, demonstrate the effectiveness of our approach. Compared to existing methods, M2BR-DPCC achieves superior performance, with an average BD-rate improvement of 95.23% over v-PCC, 12.92% over D-DPCC, and 16.16% over patchDPCC. These results underscore the potential of leveraging multi-modal data and bidirectional refinement for dynamic point cloud compression.

关键词： Point cloud compression Technological innovation image coding Accuracy Limiting Motion estimation Dynamics Estimation Filling Speech processing

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Performance evaluation of iterative tomography algorithms for incomplete projection data

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APPLIED OPTICS 2004年第7期43卷 1522-1532页

作者： Mishra, D Longtin, JP Singh, RP Prasad, v SUNY Stony Brook Dept Mech Engn Stony Brook NY 11794 USA Florida Int Univ Dept Mech Engn Miami FL 33174 USA

Projection data obtained through optical techniques for tomographic measurements, such as interferometry for refractive-index-based measurements, are often incomplete. This is due to limitations in the optical system, data storage, and alignment and vignette issues. Algebraic iterative reconstruction techniques are usually favored for such incomplete projections. A number of iterative algorithms, based on additive and multiplicative corrections, are used with a known simulated phantom and noise source to assess the reconstruction performance of incomplete data sets. In addition, we present reconstructions using experimental data obtained from a coherent gradient sensing interferometer for a steady temperature field in a fluid medium. We tested the algorithms using the simulated data set for incompleteness conditions similar to those found in the experimental data, and the best-performing algorithm is identified. (C) 2004 Optical Society of America.

关键词： Cylindrical lenses image reconstruction Laser beams Phase shift reconstruction algorithms Three dimensional measurement

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image reconstruction using symmetry

Image reconstruction using symmetry

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conference on image reconstruction from incomplete data vI

作者： Lo, v. L. Millane, R. P. Univ Canterbury Dept Elect & Comp Engn Computat Imaging Grp Christchurch 1 New Zealand

ISBN: (纸本)9780819482969

Symmetry provides a source of redundancy which can be exploited in image reconstruction. In particular, internal symmetries in molecules can help to compensate for the loss of Fourier phase information in macromolecular x-ray crystallography. Symmetry projections are incorporated into iterative projection algorithms for reconstruction of macromolecular electron densities from x-ray diffraction amplitudes from crystals. The effects of interpolation are studied and the algorithms are applied to reconstruction of an icosahedral virus.

关键词： image reconstruction symmetry x-ray crystallography phase retrieval

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3-D reconstruction OF OBJECTS from incomplete data AND A-PRIORI KNOWLEDGE.

NATO Conference Series, (Series) 4: Marine Sciences

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NATO conference Series, (Series) 4: Marine Sciences 1980年 1卷 393-395页

作者： Tsikos, Constantinos J. Bajcsy, Ruzena K.

The authors are interested in the reconstruction of 3-D objects from various sources of incomplete data as well as apriori knowledge about the object under reconstruction. As data sources use is made of conventional Xray projections as well as CAT scans perpendicular to the Xray planes. These data sources were chosen because they are complementary to each other, in the sense that information not found in one set can be found in the other.

关键词： image PROCESSING

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image reconstruction by Phase Retrieval with Transverse Translation Diversity

Image Reconstruction by Phase Retrieval with Transverse Tran...

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conference on image reconstruction from incomplete data v

作者： Guizar-Sicairos, Manuel Fienup, James R. Univ Rochester Inst Opt Rochester NY 14627 USA

ISBN: (纸本)9780819472960

Measuring a series of far-field intensity patterns from an object, taken after a, transverse translation of the object with respect to a known illumination pattern, has been shown to make the problem of image reconstruction by phase retrieval much more robust. However, previously reported reconstruction algorithms [Phys. Rev. Lett. 93, 023903 (2004)] rely oil an accurate knowledge of the translations and illumination pattern for a successful reconstruction. We developed a nonlinear optimization algorithm that allows optimization over the translations and illumination pattern, dramatically improving the reconstructions if the system parameters are inaccurately known [Opt. Express 16, 7264 (2008)]. In this paper we compare reconstructions obtained with these algorithms under realistic experimental scenarios.

关键词： Phase retrieval image reconstruction techniques inverse scattering coherent x-ray imaging

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image reconstruction in electron microscopy

Image reconstruction in electron microscopy

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conference on image reconstruction from incomplete data v

作者： Zheng, Yili Lee, Seunghee Doerschuk, Peter C. Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9780819472960

Maximum likelihood statistical algorithms are described for estimating the 3-D variation of the electron scattering intensity of biological objects from cryo electron microscopy images of multiple instances of the object. Three virus objects, two spherical and one helical, are considered. Solution of the maximum likelihood problem by expectation maximization algorithms or by direct maximization of the log likelihood requires large scale computing and end-to-end codesign of biological problem formulation, statistical models, algorithms, and software design and implementation have contributed to achieving practical results.

关键词： Cryo electron microscopy cryo EM heterogeneous spherical virus homogeneous spherical virus helical virus maximum likelihood statistical reconstruction 3-D reconstruction tomography nonlinear reconstruction expectation maximization algorithm

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