Neural Radiance Field~(NeRF) achieves extremely high quality in object-scaled and indoor scene reconstruction. However, there exist some challenges when reconstructing large-scale scenes. MLP-based NeRFs suffer from limited network capacity, while volume-based NeRFs are heavily memory-consuming when the scene resolution increases. Recent approaches propose to geographically partition the scene and learn each sub-region using an individual NeRF. Such partitioning strategies help volume-based NeRF exceed the single GPU memory limit and scale to larger scenes. However, this approach requires multiple background NeRF to handle out-of-partition rays, which leads to redundancy of learning. Inspired by the fact that the background of current partition is the foreground of adjacent partition, we propose a scalable scene reconstruction method based on joint Multi-resolution Hash Grids, named DistGrid. In this method, the scene is divided into multiple closely-paved yet non-overlapped Axis-Aligned Bounding Boxes, and a novel segmented volume rendering method is proposed to handle cross-boundary rays, thereby eliminating the need for background NeRFs. The experiments demonstrate that our method outperforms existing methods on all evaluated large-scale scenes, and provides visually plausible scene reconstruction. The scalability of our method on reconstruction quality is further evaluated qualitatively and quantitatively.
Audio-Driven Talking Face Generation with Blink Embedding and Hash Grid Landmarks Encoding
Department of Control Science and Engineering, University of Shanghai for Science and Technology（控制科学与工程系，上海科学技术大学）；School of Psychology and Neuroscience, University of Glasgow（心理学与神经科学学院，格拉斯哥大学）
Lagrangian Hashing for Compressed Neural Field Representations
Simon Fraser University（西蒙菲莎大学）；University of Trento（特伦托大学）；University of Toronto（多伦多大学）；University of British Columbia（不列颠哥伦比亚大学）；Google DeepMind（谷歌DeepMind）
Learning Heterogeneous Mixture of Scene Experts for Large-scale Neural Radiance Fields
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（计算机科学与工程系，香港科学与技术大学）；Inspur Cloud Information Technology Co, Ltd（Inspur云信息技术有限公司）
Sat-DN: Implicit Surface Reconstruction from Multi-View Satellite Images with Depth and Normal Supervision
School of Remote Sensing Information Engineering, Wuhan University（遥感信息工程学院，武汉大学）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）
Affine-Equivariant Kernel Space Encoding for NeRF Editing
Poznan University of Technology, Institute of Robotics and Machine Intelligence（波兹南技术大学机器人与机器智能研究所）；Jagiellonian University, Faculty of Mathematics and Computer Science（雅盖隆大学数学与计算机科学学院）；IDEAS Research Institute（IDEAS研究所）
ADELE - Adaptive Delaunay Grids for High-Fidelity Mesh-Native Reconstruction
ETH Zurich（苏黎世联邦理工学院）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；University of Zurich（苏黎世大学）；Balgrist University Hospital（巴尔格里斯特大学医院）
Endo-NeRF++: Uncertainty-Aware Neural Rendering with Multi-Resolution Hash Encoding for Dynamic Surgical Scene Reconstruction
Finnish Center for Artificial Intelligence (FCAI)（芬兰人工智能中心）；ELLIS Unit Helsinki（欧洲学习与智能系统研究所赫尔辛基分部）；University of Helsinki（赫尔辛基大学）；Institute for Urban Studies (Urbaria)（城市研究所）；Institute of Sustainability Science (HELSUS)（可持续发展科学研究所）
Compressive Imaging Reconstruction via Tensor Decomposed Multi-Resolution Grid Encoding
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学学院）；School of Mathematical Science, University of Electronic Science and Technology of China（电子科技大学数学科学学院）
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）
