With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise. However, in order to make use of the information provided by a variety of complimentary sensors, it is necessary to accurately calibrate them. We take advantage of recent advances in computer graphics and implicit volumetric scene representation to tackle the problem of multi-sensor spatial and temporal calibration. Thanks to a new formulation of the Neural Radiance Field (NeRF) optimization, we are able to jointly optimize calibration parameters along with scene representation based on radiometric and geometric measurements. Our method enables accurate and robust calibration from data captured in uncontrolled and unstructured urban environments, making our solution more scalable than existing calibration solutions. We demonstrate the accuracy and robustness of our method in urban scenes typically encountered in autonomous driving scenarios.
3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration
Huawei Paris Research Center（华为巴黎研究中心）；Noah’s Ark（诺亚方舟）；ICB UMR CNRS 6303, Université de Bourgogne（勃艮第大学ICB UMR CNRS 6303研究所）；Huawei London Research Center（华为伦敦研究中心）；ImViA UR 7535, Université de Bourgogne（勃艮第大学ImViA UR 7535研究所）；MIS UR 4290, Université de Picardie Jules Verne（儒勒·凡尔纳皮卡第大学MIS UR 4290研究所）
Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based Spatiotemporal Diffusion for Audio-driven Talking Portrait
University of Liverpool（利物浦大学）；Ant Group（蚂蚁集团）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Duke Kunshan University（昆山杜克大学）；Ricoh Software Research Center（理光软件研究中心）
Semi-disentangled spatiotemporal implicit neural representations of longitudinal neuroimaging data for trajectory classification
Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada（卡利告大学生物医学工程研究生项目）；Department of Radiology, University of Calgary, Calgary, Canada（卡利告大学放射科）；Department of Radiology, University of Michigan, Ann Arbor, United States（密歇根大学放射科）
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学生物医学工程学院）；School of Information Science and Technology, ShanghaiTech University, Shanghai, China（上海科技大学信息科学与技术学院）；iHuman Institute, Shanghaitech University, Shanghai, China（上海科技大学iHuman研究所）
Data-Efficient Meningioma Segmentation via Implicit Spatiotemporal Mixing and Sim2Real Semantic Injection
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；The First Affiliated Hospital of Shenzhen University（深圳大学第一附属医院）；Health Science Center（健康科学中心）；Shenzhen Second People’s Hospital（深圳第二人民医院）；Shenzhen University（深圳大学）；Department of Radiology（放射科）
EPIPTrack: Rethinking Prompt Modeling with Explicit and Implicit Prompts for Multi-Object Tracking
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学教育部长春符号计算与知识工程重点实验室）；Yangtze University（扬子大学）
An Interpretable Implicit-Based Approach for Modeling Local Spatial Effects: A Case Study of Global Gross Primary Productivity
College of Urban and Environmental Sciences, Peking University（北京大学城市与环境学院）；The Hong Kong Polytechnic University（香港理工大学）；School of Safety Science, Tsinghua University（清华大学安全科学学院）；College of Surveying and Geo-Informatics, Tongji University（同济大学测绘与地理信息学院）；School of Architecture and Urban Planning, Shenzhen University（深圳大学建筑与城市规划学院）
Flow-Guided Implicit Neural Representation for Motion-Aware Dynamic MRI Reconstruction
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所医学人工智能研究中心）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程系）
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Information Science and Technology, ShanghaiTech University（上海交通大学信息科学与技术学院）；School of Biomedical Engineering and the National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERCAMRT), Shanghai Jiao Tong University（上海交通大学生物医学工程学院和先进磁共振诊断与治疗国家工程研究中心）
Super-temporal-resolution Photoacoustic Imaging with Dynamic Reconstruction through Implicit Neural Representation in Sparse-view
School of Information Science and Technology, ShanghaiTech University（信息科学与技术学院，上海科技大学）；School of Biomedical Engineering, ShanghaiTech University（生物医学工程学院，上海科技大学）；School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University（生物医学工程学院与医学机器人研究所，上海交通大学）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（生物医学工程学院，生命科学与医学系，中国科学技术大学）；School of Engineering Science, University of Science and Technology of China（工程科学学院，中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（苏州先进研究所，中国科学技术大学）
Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能在医疗与医学中的chair，慕尼黑技术大学（TUM）和慕尼黑技术大学医院，德国慕尼黑）；School of Computation and Information Technology, Technical University of Munich, Munich, Germany（计算与信息科技学院，慕尼黑技术大学，德国慕尼黑）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany（生物医学影像中的机器学习研究所，海德堡慕尼黑，德国纽赫堡）；Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, USA（放射科，波士顿儿童医院和哈佛医学院，美国波士顿）；Munich Center for Machine Learning, Technical University of Munich, Munich, Germany（慕尼黑机器学习中心，慕尼黑技术大学，德国慕尼黑）；GE HealthCare, Munich, Germany（通用电气医疗系统，德国慕尼黑）；School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom（生物医学工程与成像科学学院，伦敦国王学院，英国伦敦）；Department of Computing, Imperial College London, London, United Kingdom（计算学院，伦敦帝国学院，英国伦敦）
本文提出 Gabor 原始要素用于加速心脏 cine MRI 重建，通过调制高斯包络实现灵活频谱支持，提升对光滑结构和锐利边界的表示能力。
MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction
Department of Bioengineering and I-X, Imperial College London（生物工程系和I-X，帝国理工学院伦敦分校）；Cardiovascular Research Centre, Royal Brompton Hospital（心脏血管研究中心，皇家布隆特医院）；National Heart and Lung Institute, Imperial College London（国家心脏和肺研究所，帝国理工学院伦敦分校）；School of Biomedical Engineering & Imaging Sciences, King’s College London（生物医学工程与成像科学学院，伦敦国王学院）；Shanghai Pudong Hospital and Human Phenome Institute, Fudan University（上海浦东医院和人类表型研究所，复旦大学）；International Human Phenome Institute (Shanghai), Shanghai, China（国际人类表型研究所（上海），上海，中国）
VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving
College of Automotive Engineering（汽车工程学院）；The National Key Laboratory of Automotive Chassis Integration and Bionics（汽车底盘集成与生物力学国家级重点实验室）；ReeFocus AI Technology（ReeFocus人工智能技术）
