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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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Realistic electric field characterization of clinically used deformable large TMS coils in a large cohort

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Brain stimulation 2025年第4期18卷 1174-1183页

作者： Torge Worbs Bianka Rumi Kristoffer H Madsen Axel Thielscher Section for Magnetic Resonance DTU Health Tech Technical University of Denmark Kgs Lyngby Denmark Danish Research Centre for Magnetic Resonance Department of Radiology and Nuclear Medicine Copenhagen University Hospital Amager and Hvidovre Copenhagen Denmark. Danish Research Centre for Magnetic Resonance Department of Radiology and Nuclear Medicine Copenhagen University Hospital Amager and Hvidovre Copenhagen Denmark Section for Visual Computing Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark Sino-Danish College University of Chinese Academy of Sciences Beijing 100190 China. Section for Visual Computing Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark. Electronic address: khma@dtu.dk.

BACKGROUND:Transcranial Magnetic Stimulation (TMS) therapies use both focal and unfocal coil designs. Unfocal designs often employ bendable windings and moveable parts, making realistic simulations of their electric fields in inter-individually varying head sizes and shapes challenging. This hampers comparisons of the various coil designs and prevents systematic evaluations of their dose-response relationships. OBJECTIVE:Introduce and validate a novel method for optimizing the position and shape of flexible coils taking individual head anatomies into account. Evaluate the impact of realistic modeling of flexible coils on the electric field simulated in the brain. METHODS:Accurate models of four coils (Brainsway H1, H4, H7; MagVenture MST-Twin) were derived from computed tomography data and mechanical measurements. A generic representation of coil deformations by concatenated linear transformations was introduced and validated. This served as basis for a principled approach to optimize the coil positions and shapes, and to optionally maximize the electric field strength in a region of interest (ROI). RESULTS:For all four coil models, the new method achieved configurations that followed the scalp anatomy while robustly preventing coil-scalp intersections on 1100 head models. In contrast, setting only the coil center positions without shape deformation regularly led to physically impossible configurations. This also affected the electric field calculated in the cortex, with a median peak difference of ∼16%. In addition, the new method outperformed grid search-based optimization for maximizing the electric field of a standard figure 8 coil in a ROI with a comparable computational complexity. CONCLUSION:Our approach alleviates practical hurdles that so far hampered accurate simulations of bendable coils. This enables systematic comparison of dose-response relationships across the various coil designs employed in therapy.

关键词： Brainsway H-coils MagVenture MST-twin coil TMS electric field calculations personalized dosing

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Retinal-NeRF: Real-time Rendering of Neural Point Fields Across Platform

Retinal-NeRF: Real-time Rendering of Neural Point Fields Acr...

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International Conference on Virtual Reality and visualization (ICVRV)

作者： Mandun Zhang Yuhan Liu Shujia Cheng Yonghui Pang Jiale Shi Zhidong Xiao Xinjiang College of Science & Technology School of Artificial Intelligence Hebei University of Technology Tianjin International Joint Center for Virtual Reality and Visual Computing School of Artificial Intelligence Hebei University of Technology Tianjin China National Centre for Computer Animation Bournemouth University Bournemouth United Kingdom

ISBN: (数字)9798331515447

ISBN: (纸本)9798331515454

Neural Radiance Fields (NeRFs) has emerged as a promising method for 3D reconstruction and novel view synthesis. However, NeRF-based methods rely on implicit encoding and heavy spatial sampling, which differ significantly from the widely used polygon mesh rasterization method. This discrepancy leads to a lack of support from common 3D software and hardware, resulting in inefficient rendering. In this work, we introduce a novel method called Retinal-NeRF, which combines point clouds and signed distance fields, for real-time rendering of 3D scenes. Our goal is achieved by converting NeRF into a representation that can be efficiently processed by standard graphics pipelines. To enhance the geometric accuracy of the scene, we propose two SDF regularization terms to improve the quality of the generated mesh. To ensure real-time rendering, we employ appearance decomposition technique to minimize the size of the MLP within the rendering pipeline. Notably, our findings indicate that the rendering speed of Retinal-NeRF is more than five times faster than existing real-time rendering techniques, without a significant loss in the quality of novel view synthesis.

关键词： Point cloud compression visualization Solid modeling Three-dimensional displays Pipelines Virtual reality Rendering (computer graphics) Neural radiance field Real-time systems Standards

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Richelieu: self-evolving LLM-based agents for AI diplomacy 24

Richelieu: self-evolving LLM-based agents for AI diplomacy

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhenyu Guan Xiangyu Kong Fangwei Zhong Yizhou Wang Institute for Artificial Intelligence Peking University College of Computer Science Beijing Information Science and Technology University and State Key Laboratory of General Artificial Intelligence BIGAI School of Artificial Intelligence Beijing Normal University and State Key Laboratory of General Artificial Intelligence BIGAI Center on Frontiers of Computing Studies School of Computer Science Nat'l Eng. Research Center of Visual Technology Peking University and Institute for Artificial Intelligence Peking University

ISBN: (纸本)9798331314385

Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and large action spaces in multi-agent tasks. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. While recent agents based on large language models (LLMs) have shown potential in various applications, they still struggle with extended planning periods in complex multi-agent settings. Leveraging recent technologies for LLM-based agents, we aim to explore AI's potential to create a human-like agent capable of executing comprehensive multi-agent missions by integrating three fundamental capabilities: 1) strategic planning with memory and reflection; 2) goal-oriented negotiation with social reasoning; and 3) augmenting memory through self-play games for self-evolution without human in the loop. Project page: https://***/view/richelieu-diplomacy.

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A Novel Smartphone Recommendation System Using Ensemble Machine Learning

A Novel Smartphone Recommendation System Using Ensemble Mach...

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2023 IEEE Asia-Pacific Conference on computer science and Data Engineering, CSDE 2023

作者： Almadhor, Ahmad Abbas, Sidra Sampedro, Gabriel Avelino Abisado, Mideth Gadekallu, Thippa Reddy College of Computer and Information Sciences Jouf University Sakaka72388 Saudi Arabia Comsats University Islamabad Department of Computer Science Islamabad Pakistan University of the Philippines Open University Faculty of Information and Communication Studies Los Baños4031 Philippines De la Salle University Center for Computational Imaging and Visual Innovations 2401 Taft Ave. Manila1004 Philippines College of Computing and Information Technologies National University Manila Philippines Zhongda Group Jiaxing City Zhejiang Province Haiyan County314312 China Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu India College of Information Science and Engineering Jiaxing University Jiaxing314001 China Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798350341072

Due to the proliferation of internet evaluations brought on by the rising demand for smartphones, consumers find it challenging to make accurate selections when purchasing. In this paper, we offer ensemble voting methods based on TF-IDF (Term Frequency-Inverse Document Frequency) features for clas-sifying mobile phone ratings. We use a recently assembled dataset comprising over 13,000 smartphone reviews from the Flipkart website. The suggested approach includes feature extraction using the TF-IDF, data cleaning, balancing, and voting-based model prediction. To identify the recently created Flipkart dataset, the suggested method created an ensemble voting mechanism based on machine learning techniques. According to the experimental findings, the suggested method performs more accurately and efficiently than conventional machine learning techniques. At 98.0 %, the model achieved the greatest accuracy. The suggested method can be expanded to additional e-commerce platforms with sizable datasets of online evaluations and assist customers in making updated purchase preferences. © 2023 IEEE.

关键词： Machine learning

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∂H: Differentiable Holography

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Laser and Photonics Reviews 2023年第9期17卷

作者： Chen, Ni Wang, Congli Heidrich, Wolfgang Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Visual Computing Center King Abdullah University of Science and Technology Jeddah Thuwal23955 Saudi Arabia

Over the past decade, the field of holography has gained significant ground due to advances in computational imaging. However, the utilization of computational tools is hampered by the mismatch between experimental setups and the conceptual model. Differentiable holography ((Formula presented.)), a novel framework for automatically self-calibrating experimental imperfections in inverse holographic imaging, is presented here. The technique is demonstrated on auto-focused complex field imaging from a single intensity-only inline hologram. © 2023 Wiley-VCH GmbH.

关键词： Inverse problems

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Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation

arXiv

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arXiv 2025年

作者： Zheng, Hong Chen, Yucheng Mu, Nan Li, Xiaoning College of Computer Science Sichuan Normal University Chengdu610101 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Department of Cardiology West China Hospital Sichuan University Chengdu610041 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610066 China Sichuan 2011 Collaborative Innovation Center for Educational Big Data Chengdu610066 China

Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regularly shaped organs, such as the left ventricle (LV) and the myocardium (MYO), whereas they perform poorly on irregularly shaped organs, such as the right ventricle (RV). In this study, we argue that this limitation of segmentation models stems from their insufficient generalization ability to address the distribution shift of segmentation targets across slices, cardiac phases, and disease conditions. To overcome this issue, we present a Multi-Disease-Aware Training Strategy (MTS) and restructure the introduced CMRI datasets into multi-disease datasets. Additionally, we propose a specialized data processing technique for preprocessing input images to support the MTS. To validate the effectiveness of our method, we performed control group experiments and cross-validation tests. The experimental results show that (1) network models trained using our proposed strategy achieved superior segmentation performance, particularly in RV segmentation, and (2) these networks exhibited robust performance even when applied to data from unknown diseases. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Unraveling the neural dynamics of mathematical interference in english reading: A novel approach with deep learning and fNIRS data

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Brain Research Bulletin 2025年 227卷 111398-111398页

作者： Liang, Zhijie Wang, Ling Su, Jianyu Sun, Bo Wang, Daifa Yang, Juan School of Computer Science Sichuan Normal University Chengdu 610066 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu 610066 China College of Foreign Languages Sichuan Normal University Chengdu 610066 China School of Biological Science and Medical Engineering Beihang University Beijing 100083 China

English has emerged as the predominant global language, driving efforts to optimize its acquisition through interdisciplinary cognitive research. While behavioral studies suggest a link between English learning and mathematical cognition, the neural mechanisms underlying this relationship remain poorly understood. To bridge this gap, the present study employs functional near-infrared spectroscopy (fNIRS) to construct a novel dataset on mathematical interference in English acquisition. Utilizing this dataset, a novel deep learning model named AC-LSTM is proposed, amalgamating Transformer and LSTM architectures to identify residual mathematical cognition during the English learning process. The AC-LSTM model achieves an exceptional accuracy rate of 99.8 %, surpassing other machine learning and deep learning models. Moreover, a multi-class classification experiment is conducted to discern algebra, geometry, and quantitative reasoning interference, with the AC-LSTM model achieving the highest accuracy of 75.9 % in this classification task. Furthermore, crucial brain channels for interference detection are pinpointed through grid search, and alterations in vital brain regions (R-Broca and L-Broca) are unveiled via association rule analysis. By integrating fNIRS, deep learning, and data mining techniques, this study delves into cognitive interference in English learning, providing valuable insights for educational neuroscience and data mining research. © 2025 The Authors

关键词： Data Mining Deep Learning English Language Learners FNIRS Mathematical Interference

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Development of Context-Sensitive Formulas to Obtain Constant Luminance Perception

Development of Context-Sensitive Formulas to Obtain Constant...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Ergun Akleman Bekir Tevfik Akgün Adil Alpkocak Visual Computing and Computational Media SVPA Texas A & M University Computer Science and Engineering College of Engineering Texas A & M University College Station ABD Bilgisayar ve Bilişim Bilimleri Fakültesi Yeditepe Üniversitesi İstanbul Türkiye Bilgisayar Mühendisliği Fakültesi İzmir Bakırçay Üniversitesi Menemen Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

In this article, we present a method to develop context-sensitive luminance correction formulas to produce constant luminance perception for a foreground object. Our formulas make the foreground object slightly transparent to mix with the blurred version of the background. This mix can be used to quickly produce any desired illusion of luminance in foreground objects based on the luminance of the background. The transparency formula has only one variable; the relative size of the foreground object. We identified the general structure of the transparency formula as a function of the relative size of the foreground object. We develop a method to intuitively control the coefficients of the transparency formula. We have implemented an interactive Web-based program in Shadertoy to identify coefficients. Using this program, we determined the coefficients of the transparency formula. We also identified a simpler affine formula, which requires only two coefficients. We made our program publicly available so that anyone can access and improve it. In this article, we also explain how to intuitively change the polynomial part of the formula. Using our explanation, users change the polynomial part of the formula to obtain their own perceptively constant luminance. This can be used as a crowd-sourcing experiment to further improve the formula.

关键词： visualization Signal processing Polynomials Object recognition

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REPRESENTING AND MODELING INCONSISTENT, IMPOSSIBLE, AND INCOHERENT SHAPES AND SCENES WITH 2D NON-CONSERVATIVE VECTOR FIELDS MAPPED ON 2-COMPLEXES

arXiv

引用

arXiv 2023年

作者： Akleman, Ergun Wang, Youyou Gönen, Özgür Visual Computing & Computational Media Texas A&M University College StationTX77831 United States Department of Computer Science and Engineering Texas A&M University College StationTX77831 United States Department of Architecture Texas A&M University College StationTX77831 United States

In this paper, we present a framework to represent mock 3D objects and scenes, which are not 3D but appear 3D. In our framework, each mock-3D object is represented using 2D non-conservative vector fields and thickness information that are mapped on 2-complexes. Mock-3D scenes are simply scenes consisting of more than one mock-3D object. We demonstrated that using this representation, we can dynamically compute a 3D shape using rays emanating from any given point in 3D. These mock-3D objects are view-dependent since their computed shapes depend on the positions of ray centers. Using these dynamically computed shapes, we can compute shadows, reflections, and refractions in real time. This representation is mainly useful for 2D artistic applications to model incoherent, inconsistent, and impossible objects. Using this representation, it is possible to obtain expressive depictions with shadows and global illumination effects. The representation can also be used to convert existing 2D artworks into a Mock-3D form that can be interactively re-rendered. © 2023, CC BY.

关键词： Luminance

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