计算机科学最新文献_第5页

3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes 3D 高斯光线追踪：快速追踪粒子场景

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2024-11-19 DOI: 10.1145/3687934

Nicolas Moenne-Loccoz, Ashkan Mirzaei, Or Perel, Riccardo de Lutio, Janick Martinez Esturo, Gavriel State, Sanja Fidler, Nicholas Sharp, Zan Gojcic

{"title":"3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes","authors":"Nicolas Moenne-Loccoz, Ashkan Mirzaei, Or Perel, Riccardo de Lutio, Janick Martinez Esturo, Gavriel State, Sanja Fidler, Nicholas Sharp, Zan Gojcic","doi":"10.1145/3687934","DOIUrl":"https://doi.org/10.1145/3687934","url":null,"abstract":"Particle-based representations of radiance fields such as 3D Gaussian Splatting have found great success for reconstructing and re-rendering of complex scenes. Most existing methods render particles via rasterization, projecting them to screen space tiles for processing in a sorted order. This work instead considers ray tracing the particles, building a bounding volume hierarchy and casting a ray for each pixel using high-performance GPU ray tracing hardware. To efficiently handle large numbers of semi-transparent particles, we describe a specialized rendering algorithm which encapsulates particles with bounding meshes to leverage fast ray-triangle intersections, and shades batches of intersections in depth-order. The benefits of ray tracing are well-known in computer graphics: processing incoherent rays for secondary lighting effects such as shadows and reflections, rendering from highly-distorted cameras common in robotics, stochastically sampling rays, and more. With our renderer, this flexibility comes at little cost compared to rasterization. Experiments demonstrate the speed and accuracy of our approach, as well as several applications in computer graphics and vision. We further propose related improvements to the basic Gaussian representation, including a simple use of generalized kernel functions which significantly reduces particle hit counts.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"14 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepDuoHDR: A Low Complexity Two Exposure Algorithm for HDR Deghosting on Mobile Devices DeepDuoHDR：用于移动设备 HDR 去噪的低复杂度双曝光算法

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2024-11-19 DOI: 10.1109/tip.2024.3497838

Kadir Cenk Alpay, Ahmet Oğuz Akyüz, Nicola Brandonisio, Joseph Meehan, Alan Chalmers

引用次数: 0

Dynamic Semantic-based Spatial-Temporal Graph Convolution Network for Skeleton-based Human Action Recognition 基于语义的动态时空图卷积网络，用于基于骨架的人体动作识别

IF 10.6 1区计算机科学

IEEE Transactions on Image Processing Pub Date : 2024-11-19 DOI: 10.1109/tip.2024.3497837

Jianyang Xie, Yanda Meng, Yitian Zhao, Nguyen Anh, Xiaoyun Yang, Yalin Zheng

引用次数: 0

Quark: Real-time, High-resolution, and General Neural View Synthesis 夸克实时、高分辨率和通用神经视图合成

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2024-11-19 DOI: 10.1145/3687953

John Flynn, Michael Broxton, Lukas Murmann, Lucy Chai, Matthew DuVall, Clément Godard, Kathryn Heal, Srinivas Kaza, Stephen Lombardi, Xuan Luo, Supreeth Achar, Kira Prabhu, Tiancheng Sun, Lynn Tsai, Ryan Overbeck

{"title":"Quark: Real-time, High-resolution, and General Neural View Synthesis","authors":"John Flynn, Michael Broxton, Lukas Murmann, Lucy Chai, Matthew DuVall, Clément Godard, Kathryn Heal, Srinivas Kaza, Stephen Lombardi, Xuan Luo, Supreeth Achar, Kira Prabhu, Tiancheng Sun, Lynn Tsai, Ryan Overbeck","doi":"10.1145/3687953","DOIUrl":"https://doi.org/10.1145/3687953","url":null,"abstract":"We present a novel neural algorithm for performing high-quality, highresolution, real-time novel view synthesis. From a sparse set of input RGB images or videos streams, our network both reconstructs the 3D scene and renders novel views at 1080p resolution at 30fps on an NVIDIA A100. Our feed-forward network generalizes across a wide variety of datasets and scenes and produces state-of-the-art quality for a real-time method. Our quality approaches, and in some cases surpasses, the quality of some of the top offline methods. In order to achieve these results we use a novel combination of several key concepts, and tie them together into a cohesive and effective algorithm. We build on previous works that represent the scene using semi-transparent layers and use an iterative learned render-and-refine approach to improve those layers. Instead of flat layers, our method reconstructs layered depth maps (LDMs) that efficiently represent scenes with complex depth and occlusions. The iterative update steps are embedded in a multi-scale, UNet-style architecture to perform as much compute as possible at reduced resolution. Within each update step, to better aggregate the information from multiple input views, we use a specialized Transformer-based network component. This allows the majority of the per-input image processing to be performed in the input image space, as opposed to layer space, further increasing efficiency. Finally, due to the real-time nature of our reconstruction and rendering, we dynamically create and discard the internal 3D geometry for each frame, generating the LDM for each view. Taken together, this produces a novel and effective algorithm for view synthesis. Through extensive evaluation, we demonstrate that we achieve state-of-the-art quality at real-time rates.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"14 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Delayed Memory Unit: Modeling Temporal Dependency Through Delay Gate 延迟存储单元：通过延迟门模拟时间依赖性

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-11-19 DOI: 10.1109/tnnls.2024.3490833

Pengfei Sun, Jibin Wu, Malu Zhang, Paul Devos, Dick Botteldooren

引用次数: 0

Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep learning 以混合深度学习为指导的生成式人工智能重塑自组装肽的发现过程

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-11-19 DOI: 10.1038/s42256-024-00928-1

Marko Njirjak, Lucija Žužić, Marko Babić, Patrizia Janković, Erik Otović, Daniela Kalafatovic, Goran Mauša

{"title":"Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep learning","authors":"Marko Njirjak, Lucija Žužić, Marko Babić, Patrizia Janković, Erik Otović, Daniela Kalafatovic, Goran Mauša","doi":"10.1038/s42256-024-00928-1","DOIUrl":"https://doi.org/10.1038/s42256-024-00928-1","url":null,"abstract":"<p>Supramolecular peptide-based materials have great potential for revolutionizing fields like nanotechnology and medicine. However, deciphering the intricate sequence-to-assembly pathway, essential for their real-life applications, remains a challenging endeavour. Their discovery relies primarily on empirical approaches that require substantial financial resources, impeding their disruptive potential. Consequently, despite the multitude of characterized self-assembling peptides and their demonstrated advantages, only a few peptide materials have found their way to the market. Machine learning trained on experimentally verified data presents a promising tool for quickly identifying sequences with a high propensity to self-assemble, thereby focusing resource expenditures on the most promising candidates. Here we introduce a framework that implements an accurate classifier in a metaheuristic-based generative model to navigate the search through the peptide sequence space of challenging size. For this purpose, we trained five recurrent neural networks among which the hybrid model that uses sequential information on aggregation propensity and specific physicochemical properties achieved a superior performance with 81.9% accuracy and 0.865 F1 score. Molecular dynamics simulations and experimental validation have confirmed the generative model to be 80–95% accurate in the discovery of self-assembling peptides, outperforming the current state-of-the-art models. The proposed modular framework efficiently complements human intuition in the exploration of self-assembling peptides and presents an important step in the development of intelligent laboratories for accelerated material discovery.</p>","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"18 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Sparse Method for Joint Range and Angle Estimation in OFDM SonarCom Systems With Phase Errors 有相位误差的 OFDM 声纳通信系统中联合范围和角度估计的稀疏方法

IF 4.4 2区计算机科学

IEEE Transactions on Aerospace and Electronic Systems Pub Date : 2024-11-19 DOI: 10.1109/taes.2024.3502005

Min Wu, Chengpeng Hao, Lihui Wang, Yongqing Wu, Danilo Orlando

引用次数: 0

A Parameterized Solution to Optimal Guidance Law Against Stationary Target With Impact Angle Constraint 带撞击角约束的静止目标最佳制导法参数化解决方案

IF 4.4 2区计算机科学

IEEE Transactions on Aerospace and Electronic Systems Pub Date : 2024-11-19 DOI: 10.1109/taes.2024.3501996

Yiji Zhu, Cong Zhou, Sai Chen, Xun Song, Kebo Li, Chaoyong Li

引用次数: 0

IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation IRS 增强型安全语义通信网络：跨层和上下文感知资源分配

IF 10.4 1区计算机科学

IEEE Transactions on Wireless Communications Pub Date : 2024-11-19 DOI: 10.1109/twc.2024.3495720

Lingyi Wang, Wei Wu, Fuhui Zhou, Zhijin Qin, Qihui Wu

引用次数: 0

Enhanced Multiuser Space-Time Line Code for Downlink Multiple Antenna Transmission 用于下行链路多天线传输的增强型多用户时空线码

IF 8.3 2区计算机科学

IEEE Transactions on Communications Pub Date : 2024-11-19 DOI: 10.1109/tcomm.2024.3502460

Yundong Kim, Sumin Han, Jingon Joung, Juyeop Kim, Jian Zhao, Jihoon Choi

引用次数: 0