Neural Radiance Fields (NeRFs) have emerged as a groundbreaking paradigm for representing 3D objects and scenes by encoding shape and appearance information into the weights of a neural network. Recent studies have demonstrated that these weights can be used as input for frameworks designed to address deep learning tasks; however, such frameworks require NeRFs to adhere to a specific, predefined architecture. In this paper, we introduce the first framework capable of processing NeRFs with diverse architectures and performing inference on architectures unseen at training time. We achieve this by training a Graph Meta-Network within an unsupervised representation learning framework, and show that a contrastive objective is conducive to obtaining an architecture-agnostic latent space. In experiments conducted across 13 NeRF architectures belonging to three families (MLPs, tri-planes, and, for the first time, hash tables), our approach demonstrates robust performance in classification, retrieval, and language tasks involving multiple architectures, even unseen at training time, while also matching or exceeding the results of existing frameworks limited to single architectures. Our code and data are available at https://cvlab-unibo.github.io/gmnerf.
Dual-Space NeRF: Learning Animatable Avatars and Scene Lighting in Separate Spaces
ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）；Shanghai Engineering Research Center of Energy Efficient and Custom AI IC（上海能效与定制人工智能集成电路工程技术研究中心）
C-NERF: Representing Scene Changes as Directional Consistency Difference-based NeRF
College of Computer Science and Technology, Civil Aviation University of China（中国民航大学计算机科学与技术学院）；IHPC, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；CFAR, Agency for Science, Technology and Research（新加坡科技研究局CFAR研究所）
UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving
University of Science and Technology of China（中国科学技术大学）；PKU-Wuhan Institute for Artificial Intelligence（北大武汉人工智能研究院）；DJI Technology（大疆科技）；ShanghaiTech University（上海科技大学）
Few-shot NeRF by Adaptive Rendering Loss Regularization
Nanyang Technological University（南洋理工大学）；Huazhong University of Science and Technology（华中科技大学）；Centre for Frontier AI Research（前沿人工智能研究中心）；A*STAR（新加坡科技研究局）
NeRF-MIR: Towards High-Quality Restoration of Masked Images with Neural Radiance Fields
Shanghai Key Lab of Intelligent Information Processing（上海智能信息处理重点实验室）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
Radiance Field Learners As UAV First-Person Viewers
Hangzhou Dianzi University（杭州电子科技大学）；Meta AI；Harvard University（哈佛大学）；Kent State University（肯特州立大学）；NVIDIA（英伟达公司）；Rochester Institute of Technology（罗切斯特理工学院）
Can we make NeRF-based visual localization privacy-preserving?
Faculty of Electrical Engineering, Czech Technical University in Prague（捷克技术大学布拉格电子工程系）；Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（捷克信息学、机器人学与自动控制研究所，捷克技术大学布拉格）；NAVER LABS Europe（NAVER LABS欧洲）
CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations
Department of Electrical Engineering (ELEN), ICTEAM, UCLouvain（电子工程系（ELEN），ICTEAM，鲁汶大学）；Department of Electrical Engineering (ESAT), KU Leuven（电子工程系（ESAT），鲁汶大学）；Department of Mechanical Engineering (MECH), KU Leuven（机械工程系（MECH），鲁汶大学）；Aerospacelab（航天实验室）
Neural Radiance Field-based Visual Rendering: A Comprehensive Review
National Innovation Center for Digital Fishery（国家数字渔业创新中心）；Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs（农业农村部水产动物与畜禽智慧养殖技术重点实验室）；Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University（中国农业大学农业信息获取技术重点实验室）；College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）；College of Engineering, China Agricultural University（中国农业大学工学院）
Zero-Shot 3D Plant Organ Segmentation with SAM3 and Semantic NeRFs
Fraunhofer Institute for Integrated Circuits (IIS)（弗劳恩霍夫集成电路研究所）；Otto-Friedrich-Universität Bamberg（班贝格奥托·弗里德里希大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
JacobiNeRF: NeRF Shaping with Mutual Information Gradients
Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；Stanford University（斯坦福大学）；NVIDIA Research（英伟达研究院）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Qizhi Institute（上海期智研究院）；Google Research（谷歌研究院）
Explicit-NeRF-QA: A Quality Assessment Database for Explicit NeRF Model Compression
Cooperative MediaNet Innovation Center, Shanghai Jiao Tong University（上海交通大学合作媒体网络创新中心）；Department of Computer Science and Electrical Engineering, University of Missouri-Kansas City（密苏里大学堪萨斯城分校计算机科学与电气工程系）
