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International conference on Computer Graphics, visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big data Analytics, data Mining and Computational Intelligence 2021, BIGdACI 2021 - Held at the 15th Multi-conference on Computer Science and Information Systems, MCCSIS 2021

International Conference on Computer Graphics, Visualization...

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International conference on Computer Graphics, visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big data Analytics, data Mining and Computational Intelligence 2021, BIGdACI 2021 - Held at the 15th Multi-conference on Computer Science and Information Systems, MCCSIS 2021

ISBN: (纸本)9789898704320

The proceedings contain 24 papers. The topics discussed include: using fast multidimensional projections to reveal band and circumplex patterns in reorderable matrices;a 3d spectral-spatial classification of hyperspectral remote sensing imagery using inception based network;3d face reconstruction from hard blended edges;capsule neural networks in classification of skin lesions;a single RGB image based 3d object reconstruction system;face features-based personality assessment;analysis of capsule networks for image classification;analysis and visual exploration of prediction algorithms for public bicycle sharing systems;and hello, my name is smarttram, human factors is on board, enjoy the ride! developing a human factors program for automatic trams.

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APPLICATION OF TOMOGRAPHIC STATIC CORRECTION TECHNOLOGY CONSTRAINEd BY MICRO LOGGING IN 3d SPLICING PROCESSING 82

APPLICATION OF TOMOGRAPHIC STATIC CORRECTION TECHNOLOGY CONS...

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82nd EAGE conference and Exhibition 2021

作者： Li, M. Li, F. Wang, Y. Xu, L. Cao, Y. Petrochina Changqing Oilfield Company China

ISBN: (纸本)9781713841449

In this paper, aiming at the static correction problems that affect the structure shape and stack imaging in 3d splicing processing, a new tomographic static correction technology constrained by micro logging is explored, which is suitable for the transition area of complex desert Loess Plateau in Ordos Basin. Wavelet transform is applied to the decomposition and reconstruction of velocity model to improve the reliability of inversion. This method solves the static correction problem of 3d splicing data and improves the quality of seismic imaging. © (2021) by the European Association of Geoscientists & Engineers (EAGE)All rights reserved.

关键词： Wavelet decomposition

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GANESH: Generalizable NeRF for Lensless Imaging

GANESH: Generalizable NeRF for Lensless Imaging

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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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Live Semantic 3d Perception for Immersive Augmented Reality

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IEEE TRANSACTIONS ON visualization ANd COMPUTER GRAPHICS 2020年第5期26卷 2012-2022页

作者： Han, Lei Zheng, Tian Zhu, Yinheng Xu, Lan Fang, Lu Tsinghua Univ Beijing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

Semantic understanding of 3d environments is critical for both the unmanned system and the human involved virtual/augmented reality (VR/AR) immersive experience. Spatially-sparse convolution, taking advantage of the intrinsic sparsity of 3d point cloud data, makes high resolution 3d convolutional neural networks tractable with state-of-the-art results on 3d semantic segmentation problems. However, the exhaustive computations limits the practical usage of semantic 3d perception for VR/AR applications in portable devices. In this paper, we identify that the efficiency bottleneck lies in the unorganized memory access of the sparse convolution steps, i.e., the points are stored independently based on a predefined dictionary, which is inefficient due to the limited memory bandwidth of parallel computing devices (GPU). With the insight that points are continuous as 2d surfaces in 3d space, a chunk-based sparse convolution scheme is proposed to reuse the neighboring points within each spatially organized chunk. An efficient multi-layer adaptive fusion module is further proposed for employing the spatial consistency cue of 3d data to further reduce the computational burden. Quantitative experiments on public datasets demonstrate that our approach works 11x faster than previous approaches with competitive accuracy. By implementing both semantic and geometric 3d reconstruction simultaneously on a portable tablet device, we demo a foundation platform for immersive AR applications.

关键词： Three-dimensional displays Convolution Semantics Two dimensional displays Image segmentation Graphics processing units Solid modeling dense 3d reconstruction 3d Semantic Segmentation 3d Convolutional Network virtual Reality Augmented Reality

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An Athlete's Foot data Platform with 3d Point Cloud Processing and Management Technology 21

An Athlete's Foot Data Platform with 3D Point Cloud Processi...

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IEEE/ACIS 21st International Fall virtual conference on Computer and Information Science (ICIS Fall)

作者： Wang, Yanyan Li, Chunfang Sun, Jiangnan Li, Min Yang, Jintian Commun Univ China Sch Comp Sci & Cybersecur Beijing Peoples R China Hebei Univ Econ & Trade Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781728176796

driven by the application needs of computer-aided geometric design, computer animation, reverse engineering, medical diagnosis and entertainment industry, 3d point cloud data processing technology has attracted more and more attention. This article described some technology to process and manage the 3d point cloud data of athlete's feet, and the process of building a 3d data integrated system of athlete's foot information which consists of data acquisition, processing, upload, storage, visualization and authority control. Python Open3d is used to merge the foot mold point cloud data with base information, extract the foot mold point cloud data without base, denoise and generate mesh models. *** technology is used to realize the smooth display and interaction of the model in Vue3 framework and ***. The system can be used for assisting researchers in the foot modeling and sports hiomechanical analysis of Winter Olympic athletes and sports students.

关键词： 3d point cloud denoising and smoothing surface reconstruction 3d risualization

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PVT: An Implicit Surface reconstruction Framework via Point Voxel Geometric-Aware Transformer

PVT: An Implicit Surface Reconstruction Framework via Point ...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Chuanmao Fan Chenxi Zhao Ye duan University of Missouri-Columbia Columbia MO USA Clemson University Clemson SC USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

3d surface reconstruction from unorganized point clouds is a fundamental task in visual computing with numerous applications in areas such as robotics, virtual reality, augmented reality, and animation. To date, many deep learning-based surface reconstruction methods have been proposed, demonstrating great performance on many benchmark datasets. Among these, neural implicit field learning-based methods have gained popularity for their capability of representing complex structures in a continuous implicit distance field. Existing neural implicit field learning methods either utilize voxelized point cloud then feed them to a deep network, or directly take points as input. In this paper, we propose an implicit surface reconstruction framework based on point voxel geometric-aware transformer PVT to seamlessly integrate point-based convolution with voxel-based convolution using bidirectional transformers. Experiments show that the proposed PVT framework can better encode local geometry details and provide a significant performance boost over existing state-of-the-art methods.

关键词： Learning systems Point cloud compression Geometry Surface reconstruction Three-dimensional displays Shape Convolution Benchmark testing reconstruction algorithms Transformers

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Cultural Heritage Assets Optimization Workflow for Interactive System development 46

Cultural Heritage Assets Optimization Workflow for Interacti...

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46th Annual IEEE-Computer-Society International Computers, Software, and Applications conference (COMPSAC) - Computers, Software, and Applications in an Uncertain World

作者： Chai, Kedong Li, Yue Xian Jiaotong Liverpool Univ Design Sch Suzhou Peoples R China Xian Jiaotong Liverpool Univ Dept Comp Sch Adv Technol Suzhou Peoples R China

ISBN: (纸本)9781665488105

An increasing number of reconstructed digital assets are being created worldwide to preserve cultural heritage. These assets can be used in interactive systems such as augmented reality (AR) and virtual reality (VR) to provide effective ways to access and learn about cultural heritage. One of the widely adopted reconstruction techniques is close-range photogrammetry. However, scanned models need to be processed and optimized before they can be used in interactive systems, which requires a series of retopology and baking work to reduce the size of models while maintaining visual fidelity. Nevertheless, manual retopology and baking are complex processes. An efficient optimization workflow is essential for the use of cultural heritage assets in interactive systems. This paper presents an optimization workflow for retopology and texture baking using free and open-source software. Evaluations show that the workflow demonstrates its strengths in its high efficiency, versatility, learnability, and low cost. This work contributes insights to researchers and practitioners in the field of cultural heritage.

关键词： digital heritage interactive system VR AR 3d modelling retopology texture baking photogrammetry

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Crime Scene Testimony in virtual Reality Applicability Assessment

Crime Scene Testimony in Virtual Reality Applicability Asses...

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Circuits, Systems, Communications and Computers (CSCC), International conference on

作者： Jiri Sevcik Milan Adamek Vaclav Mach Faculty of Applied Informatics Tomas Bata Unversity in Zlin Zlin Czech Republic

ISBN: (纸本)9781665481878

3d reconstruction represents sophisticated approach to crime scene documentation and farther forensic analysis; however, quality of results is strictly depended on source data. This article identifies feasibility limits and defines critical requirements for crime scene 3d reconstruction. An evaluation triangle is proposed to realize method applicability assessment. Moreover, individual factors influencing the feasibility were described in detail and their mutual relationship has been formulated. A crime scene testimony in virtual reality workflow was provided to visualize individual process relation.

关键词： Computers Three-dimensional displays Forensics data visualization virtual reality documentation

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Crowd3d: Towards Hundreds of People reconstruction from a Single Image

Crowd3D: Towards Hundreds of People Reconstruction from a Si...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Wen Jing Huang Huili Cui Haozhe Lin Yu-Kun Lai Lu Fang Kun Li Tianjin University China Tsinghua University China Cardiff University United Kingdom

Image-based multi-person reconstruction in wide-field large scenes is critical for crowd analysis and security alert. However, existing methods cannot deal with large scenes containing hundreds of people, which encounter the challenges of large number of people, large variations in human scale, and complex spatial distribution. In this paper, we propose Crowd3d, the first framework to reconstruct the 3d poses, shapes and locations of hundreds of people with global consistency from a single large-scene image. The core of our approach is to convert the problem of complex crowd localization into pixel localization with the help of our newly defined concept, Human-scene virtual Interaction Point (HVIP). To reconstruct the crowd with global consistency, we propose a progressive reconstruction network based on HVIP by pre-estimating a scene-level camera and a ground plane. To deal with a large number of persons and various human sizes, we also design an adaptive human-centric cropping scheme. Besides, we contribute a benchmark dataset, LargeCrowd, for crowd reconstruction in a large scene. Experimental results demonstrate the effectiveness of the proposed method. The code and the dataset are available at http://***/faculty/likun/projects/Crowd3d.

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FusedWSS: Water Surface Segmentation Fusing Machine Learning and Geometric Cues

FusedWSS: Water Surface Segmentation Fusing Machine Learning...

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International conference on Information Fusion

作者： Jon Torgeir Grini Rudolf Mester Trym Anthonsen Nygård Nicholas dalhaug Edmund Førland Brekke Annette Stahl Department of Engineering Cybernetics NTNU Trondheim Norway Department of Computer Science NTNU Trondheim Norway

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Navigating unmanned surface vehicles (USVs) in urban waterways presents unique challenges due to irregular waterlines, obstacles, and reflections in the water. determining the collision-free navigable area is crucial to enable safe USV operation. This paper introduces Fused Water Surface Segmentation (FusedWSS), a novel approach to water surface segmentation that aims to enhance navigation capabilities for USVs in complex harbor environments using a stereo camera. The method locates the water plane by performing plane fitting with outlier rejection and plane validation on the reconstructed 3d point cloud. From the plane parameters, the virtual horizon line is inferred and used for point cloud and image cropping. The water surface mask and virtual horizon line are fused with a deep learningbased semantic segmentation method to produce accurate and reliable water masks for each image frame. Additional refinement of the water mask is performed using detected obstacle masks. Validation was carried out using data from the MilliAmpere 2 autonomous ferry prototype in Trondheim, Norway, and a publicly available maritime dataset, demonstrating the efficacy of the methods.

关键词： Point cloud compression Surface reconstruction Three-dimensional displays Accuracy Navigation Fitting Cameras Surface fitting Image reconstruction Autonomous vehicles

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