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

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 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. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj Computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

Arrow: A RISC-V vector accelerator for machine learning inference

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arXiv 2021年

作者： Al Assir, Imad El Iskandarani, Mohamad Al Sandid, Hadi Rayan Saghir, Mazen A.R. Embedded and Reconfigurable Computing Lab Department of Electrical and Computer Engineering American University of Beirut Beirut Lebanon

In this paper we present Arrow, a configurable hardware accelerator architecture that implements a subset of the RISC-V v0.9 vector ISA extension aimed at edge machine learning inference. Our experimental results show that an Arrow co-processor can execute a suite of vector and matrix benchmarks fundamental to machine learning inference 2 – 78× faster than a scalar RISC processor while consuming 20% – 99% less energy when implemented in a Xilinx XC7A200T-1SBG484C FPGA. © 2021, CC BY.

关键词： computer architecture

Synthetic Data at Scale: A Paradigm to Efficiently Leverage Machine Learning in Agriculture

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SSRN

SSRN 2023年

作者： Klein, Jonathan Waller, Rebekah E. Pirk, Sören Palubicki, Wojtek Tester, Mark Michels, Dominik L. Visual Computing Center KAUST Campus 23955-6900 Saudi Arabia Center for Desert Agriculture KAUST Campus 23955-6900 Saudi Arabia Adobe Research San JoseCA95110-2704 United States UAM EU Poznan61-614 Poland Department of Computer Electrical and Mathematical Sciences & Engineering

The rise of artificial intelligence (AI) and in particular modern machine learning (ML) algorithms has been one of the most exciting developments in agriculture within the last decade. While undisputedly powerful, their main drawback remains the need for sufficient and diverse training data. The collection of real datasets and their annotation are the main cost drivers of ML developments, and while promising results on synthetically generated training data have been shown, their generation is not without difficulties on their own. In this contribution, we present a paradigm for the iterative cost-efficient generation of synthetic training data. Its application is demonstrated by developing a low-cost early disease detector for tomato plants (Solanum lycopersicum) using synthetic training data. In particular, a binary classifier is developed to distinguish between healthy and infected tomato plants based on photographs taken by an unmanned aerial vehicle (UAV) in a greenhouse complex. The classifier is trained by exclusively using synthetic images which are generated iteratively to obtain optimal performance. In contrast to other approaches that rely on a human assessment of similarity between real and synthetic data, we instead introduce a structured, quantitative approach. We find that our approach leads to a more cost efficient use of ML-aided computer vision tasks in agriculture. © 2023, The Authors. All rights reserved.

关键词： Machine learning

Relative Spherical-visual Localization for Cooperative Unmanned Aerial Systems

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Relative Spherical-Visual Localization for Cooperative Unman...

2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Holter, Steffen Tsoukalas, Athanasios Evangeliou, Nikolaos Giakoumidis, Nikolaos Tzes, Anthony Imperial College London Department of Computing London United Kingdom Lab New York University Abu Dhabi Electrical Computer Engineering Abu Dhabi129188 United Arab Emirates Abu Dhabi129188 United Arab Emirates

ISBN: (纸本)9780738131153

Cooperative Unmanned Aerial Systems (UASs) in GPS-denied environments demand an accurate pose-localization system to ensure efficient operation. In this paper we present a novel visual relative localization system capable of monitoring a 360° Field-of-View (FoV) in the immediate surroundings of the UAS using a spherical camera. Collaborating UASs carry a set of fiducial markers which are detected by the camera-system. The spherical image is partitioned and rectified into a set of square images. An algorithm is proposed to select the number of images that balances the computational load while maintaining a minimum tracking-accuracy level. The developed system tracks UASs in the vicinity of the spherical camera and experimental studies using two UASs are offered to validate the performance of the relative visual localization against that of a motion capture system. © 2021 IEEE.

关键词： Cameras

Mean-field games among teams

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arXiv 2023年

作者： Subramanian, Jayakumar Kumar, Akshat Mahajan, Aditya Media and Data Science Research Lab Digital Experience Cloud Adobe Inc. Uttar Pradesh Noida India School of Computing and Information Systems The Singapore Management University Singapore Department of Electrical and Computer Engineering McGill University Montreal Canada

In this paper, we present a model of a game among teams. Each team consists of a homogeneous population of agents. Agents within a team are cooperative while the teams compete with other teams. The dynamics and the costs are coupled through the empirical distribution (or the mean field) of the state of agents in each team. This mean-field is assumed to be observed by all agents. Agents have asymmetric information (also called a non-classical information structure). We propose a mean-field based refinement of the Team-Nash equilibrium of the game, which we call mean-field Markov perfect equilibrium (MF-MPE). We identify a dynamic programming decomposition to characterize MF-MPE. We then consider the case where each team has a large number of players and present a mean-field approximation which approximates the game among large-population teams as a game among infinite-population teams. We show that MF-MPE of the game among teams of infinite population is easier to compute and is an Ε-approximate MF-MPE of the game among teams of finite population. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamic programming

Leveraging Randomization-Inspired Hybrid Deep Learning for Enhanced Diabetic Retinopathy Detection Via Multi-Scale Image Analysis

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SSRN

SSRN 2024年

作者： Mutawa, A.M. Hemalakshmi, G.R. Prakash, N.B. Murugappan, M. Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat13060 Kuwait School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electrical and Electronics Engineering National Engineering College Tamilnadu Kovilpatti India Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha13133 Kuwait Department of Electronics and Communication Engineering Faculty of Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai600117 India Universiti Malaysia Perlis Perlis Kangar02600 Malaysia

Diabetic retinopathy (DR), a critical complication of diabetes, significantly increases the risk of vision loss. Prompt detection is essential for effective intervention, yet existing diagnostic practices are resource-intensive, inconsistent, and often inaccessible. This study pioneers an innovative framework, leveraging a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, synergized with Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, to fortify the detection of DR. Inspired by principles of randomization-based learning, our approach incorporates elements of stochastic modeling within the CNN-RBF architecture to optimize feature extraction and classification, mirroring the efficiency of non-iterative training processes. Through sophisticated image preprocessing, including advanced noise reduction and morphological techniques, complemented by Otsu’s thresholding for precise blood vessel segmentation, we enhance the model's diagnostic capability. Our methodology exhibits superior performance in DR screening, achieving benchmarks of 96.10% precision, 95.35% sensitivity, 97.06% specificity, and 96.10% accuracy, markedly surpassing conventional diagnostic methods. Applied across various publicly available datasets, this research not only advances the precision of DR diagnostics but also contributes to the broader discourse on the potential of hybrid, randomization-inspired neural networks in medical imaging. This fusion of deep learning innovation with the principles of randomization-based algorithms opens new avenues for developing accessible, accurate diagnostic tools, potentially alleviating the global impact of diabetic vision loss.. © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series 11

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A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pande...

11th International Conference on computer engineering and Knowledge, ICCKE 2021

作者： Taheri, Ahamd Ghashghaei, Shahriar Beheshti, Amin Rahimizadeh, Keyvan Yasouj University Departement of Electrical and Computer Engineering Yasouj Iran Tehran University of Medical Sciences School of Medicine Tehran Iran Macquarie University AIP Research Centre Data Analytics Lab Department of Computing SydneyNSW2109 Australia

ISBN: (纸本)9781665402088

In this paper, a novel hybrid method called DMHS-GMDH is presented to predict the time series of COVID-19 outbreaks. In this way, a new version of Harmony Search (HS) algorithm, named Double Memory HS (DMHS), is designed to optimize the structure of a Group Method of Data Handling (GMDH) type neural network. We conduct a series of experiments by applying proposed method on real COVID-19 dataset to forecast new cases and deaths of COVID-19. The statistical analysis indicates that the DMHS-GMDH algorithm on average provides better results than other competitors and the results demonstrate how our approach at least improves coefficient of determination and RMSE by 21% and 45%, respectively. © 2021 IEEE.

关键词： COVID-19