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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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OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation

arXiv

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arXiv 2023年

作者： Teng, Fei Zhang, Jiaming Peng, Kunyu Wang, Yaonan Stiefelhagen, Rainer Yang, Kailun The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The Institute for Visual Computing ETH Zurich Zurich8092 Switzerland

Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: (1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. (2) A relative displacement difference exists in the data collected by different micro-lenses. To address these issues, we propose an Omni-Aperture Fusion model (OAFuser) that leverages dense context from the central view and extracts the angular information from sub-aperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective Sub-Aperture Fusion Module (SAFM). This module efficiently embeds sub-aperture images in angular features, allowing the network to process each sub-aperture image with a minimal computational demand of only (∼1GFlops). Furthermore, to address the mismatched spatial information across viewpoints, we present a center Angular Rectification Module (CARM) to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of all evaluation metrics and sets a new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain of +3.69%. The source code for OAFuser is available at https://***/FeiBryantkit/OAFuser. Impact Statement-To solve the data abundance problem, we have reduced the significant computational consumption of light field cameras while not introducing any additional parameters. The proposed method has practical value for the deployment and application of light field cameras. The proposed

关键词： Semantic Segmentation

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Autonomous Vision-Guided Resection of Central Airway Obstruction

arXiv

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arXiv 2025年

作者： Smith, Mariana E. Yilmaz, Nural Watts, Tanner Scheikl, Paul Maria Ge, Jiawei Deguet, Anton Kuntz, Alan Krieger, Axel Department of Mechanical Engineering Johns Hopkins University BaltimoreMD21211 United States Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States Malone Center for Engineering and Healthcare Johns Hopkins University BaltimoreMD21211 United States

Existing tracheal tumor resection methods often lack the precision required for effective airway clearance, and robotic advancements offer new potential for autonomous resection. We present a vision-guided, autonomous approach for palliative resection of tracheal tumors. This system models the tracheal surface with a fifth-degree polynomial to plan tool trajectories, while a custom Faster R-CNN segmentation pipeline identifies the trachea and tumor boundaries. The electrocautery tool angle is optimized using handheld surgical demonstrations, and trajectories are planned to maintain a 1 mm safety clearance from the tracheal surface. We validated the workflow successfully in five consecutive experiments on ex-vivo animal tissue models, successfully clearing the airway obstruction without trachea perforation in all cases (with more than 90% volumetric tumor removal). These results support the feasibility of an autonomous resection platform, paving the way for future developments in minimally-invasive autonomous resection. Copyright © 2025, The Authors. All rights reserved.

关键词： Transplantation (surgical)

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection 22

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

引用

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yang, Fan Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei 230026 China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou 215123 China Key Laboratory of Precision and Intelligent Chemistry Ustc Anhui Hefei 230026 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Cas Beijing 100190 China

ISBN: (纸本)9798331520526

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment. © 2025 IEEE.

关键词： Anatomical Landmark Detection Unsupervised Domain Adaptation

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Concept and Considerations for the Development of an Information Database for Efficient International Freight Transport

Concept and Considerations for the Development of an Informa...

引用

International Conference on Computational Cybernetics (ICCC)

作者： Petra Opitz Tamás Haidegger Enikö Nagy John von Neumann Faculty of Informatics Óbuda University Budapest Hungary University Research and Innovation Center (EKIK) Óbuda University Budapest Hungary School of Computing Queen's University Kingston ON John von Neumann Faculty of Informatics Óbuda University Antal Bejczy Center for Intelligent Robotics Budapest Hungary

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

International freight transport is a growing market and a crucial component of the global economy, enabling the seamless flow of goods across regions and supporting global trade. However, the industry faces significant challenges, particularly in road transport orchestration, due to the increasing complexity of operations and the demand for real-time decision-making. This study sketched the algorithmic and programming framework to address these challenges by presenting an optimized database architecture designed to improve decision support systems for freight transport. The proposed system architecture may leverage the flexibility and scalability of NoSQL databases to manage large, semi-structured datasets in the future, while REST APIs enable seamless integration and real-time data manipulation. To enhance usability and transparency, the system incorporates advanced data visualization tools, allowing stakeholders to derive actionable insights and improve operational efficiency. By focusing on areas such as real-time tracking, sustainability, and process optimization, this work not only addresses current operational needs but also provides a robust foundation for future developments, including advanced analytics and mobile application integration. This contribution supports the logistics industry's shift toward more adaptive, efficient, and sustainable transport solutions.

关键词： Industries NoSQL databases Data visualization Systems architecture Real-time systems Visual databases Usability Sustainable development Optimization Logistics

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Rethinking Dual-Domain Undersampled MRI Reconstruction: Domain-Specific Design from the Perspective of the Receptive Field

Rethinking Dual-Domain Undersampled MRI Reconstruction: Doma...

引用

IEEE International Symposium on Biomedical Imaging

作者： Ziqi Gao S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei P.R. China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research University of Science and Technology of China Suzhou P.R. China Institute of Computing Technology Chinese Academy of Sciences

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Undersampled MRI reconstruction is crucial for accelerating clinical scanning. Dual-domain reconstruction networks are performant among SoTA deep learning methods by leveraging complementary information of two domains. In this paper, we rethink dual-domain network design from the perspective of the receptive field. We formulate k-space reconstruction as an interpolation problem and use the probability for a successful interpolation given a receptive field as a heuristic for building k-space networks. We divide image recovery into two sub-problems: artifact removal and detail refinement, corresponding to a global and a local task, respectively. To this end, we introduce two domain-specific modules for dual-domain networks, namely k-space global initialization and image-domain parallel local detail enhancement. We evaluate our modules by translating a SoTA method Du-DoRNet under different conventions of MRI reconstruction including image-domain, dual-domain, and reference-guided reconstruction on the public IXI dataset. Our model DuDoR-Net+ achieves significant improvements over competing deep learning methods.

关键词： Deep learning Interpolation Magnetic resonance imaging Biological system modeling Buildings Task analysis Image reconstruction

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Learning Continuous 3D Reconstructions for Geometrically Aware Grasping

Learning Continuous 3D Reconstructions for Geometrically Awa...

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2020 IEEE International Conference on robotics and Automation, ICRA 2020

作者： Van Der Merwe, Mark Lu, Qingkai Sundaralingam, Balakumar Matak, Martin Hermans, Tucker University of Utah School of Computing and Robotics Center Salt Lake CityUT84112 United States

ISBN: (纸本)9781728173955

Deep learning has enabled remarkable improvements in grasp synthesis for previously unseen objects from partial object views. However, existing approaches lack the ability to explicitly reason about the full 3D geometry of the object when selecting a grasp, relying on indirect geometric reasoning derived when learning grasp success networks. This abandons explicit geometric reasoning, such as avoiding undesired robot object collisions. We propose to utilize a novel, learned 3D reconstruction to enable geometric awareness in a grasping system. We leverage the structure of the reconstruction network to learn a grasp success classifier which serves as the objective function for a continuous grasp optimization. We additionally explicitly constrain the optimization to avoid undesired contact, directly using the reconstruction. We examine the role of geometry in grasping both in the training of grasp metrics and through 96 robot grasping trials. Our results can be found on https://***/view/reconstruction-grasp/. © 2020 IEEE.

关键词： Geometry

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation 22

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

引用

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Zhao, Mingyue Qiu, Xiaolan Zhou, S.Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research Ustc China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (纸本)9798331520526

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo. © 2025 IEEE.

关键词： Airway extraction dense topological perception explicit topological modeling sparse topological learning

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DULA and DEBA: Differentiable Ergonomic Risk Models for Postural Assessment and Optimization in Ergonomically Intelligent pHRI

arXiv

引用

arXiv 2022年

作者： Yazdani, Amir Novin, Roya Sabbagh Merryweather, Andrew Hermans, Tucker Department of Mechanical Eng. and Robotics Center University of Utah Salt Lake CityUT United States School of Computing and Robotics Center University of Utah Salt Lake CityUT United States NVIDIA SeattleWA United States

Ergonomics and human comfort are essential concerns in physical human-robot interaction applications. Defining an accurate and easy-to-use ergonomic assessment model stands as an important step in providing feedback for postural correction to improve operator health and comfort. Common practical methods in the area suffer from inaccurate ergonomics models in performing postural optimization. In order to retain assessment quality, while improving computational considerations, we propose a novel framework for postural assessment and optimization for ergonomically intelligent physical human-robot interaction. We introduce DULA and DEBA, differentiable and continuous ergonomics models learned to replicate the popular and scientifically validated RULA and REBA assessments with more than 99% accuracy. We show that DULA and DEBA provide assessment comparable to RULA and REBA while providing computational benefits when being used in postural optimization. We evaluate our framework through human and simulation experiments. We highlight DULA and DEBA's strength in a demonstration of postural optimization for a simulated pHRI task. Copyright © 2022, The Authors. All rights reserved.

关键词： Risk assessment

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Exploring Deep Object Content through In-hand Tactile Vibration Sensing with Shaking Manipulation

Exploring Deep Object Content through In-hand Tactile Vibrat...

引用

International Conference on Information and Communications Technology (ICOIACT)

作者： Muhammad Arifin Rian Putra Pratama Abdullah Iskandar Rezi Rafidan Alfizar Taufik Ibnu Salim Aris Munandar Ananta Adhi Wardana Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia School of Computing Telkom University Bandung Indonesia Department of Physics Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Robotics and Artificial Intelligence Engineering Airlangga University Surabaya Indonesia

ISBN: (数字)9798331536206

ISBN: (纸本)9798331536213

Tactile sensing provides robots with insights beyond visual limitations, enabling the detection of surface characteristics and deformation properties like texture and slip. However, identifying internal content conditions remains a challenge. This study introduces a tactile-driven approach for classifying the internal content of various containers through vibration sensing and controlled shaking manipulation. By dynamically altering the mass distribution within the object, the system generates distinct changes in the center of mass (CoM), resulting in unique vibrational patterns. These patterns are captured and processed by a Convolutional Neural Network (CNN) to categorize the content state as depleted, partially loaded, or maximally loaded. Experimental results demonstrate a classification accuracy of 93 %, with clear clustering of content conditions, validating the effectiveness of this tactile-based approach across different fill levels. This tactile-based method significantly enhances robotic perception in tasks requiring internal content knowledge, such as sorting, recycling, and storage, offering a robust and scalable solution for real-world applications.

关键词： Vibrations Visualization Accuracy Fast Fourier transforms Containers Robot sensing systems Sensors Surface texture Convolutional neural networks Sorting

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