3D 视觉

Neural Sparse Voxel Fields

Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, Christian Theobalt

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV、cs.GR

Comments 20 pages, in progress

Learning Compositional Radiance Fields of Dynamic Human Heads

Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, Michael Zollhöfer

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV、cs.GR

Object-Centric Neural Scene Rendering

Michelle Guo, Alireza Fathi, Jiajun Wu, Thomas Funkhouser

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV、cs.GR

Comments Summary Video: https://youtu.be/NtR7xgxSL1U Project Webpage: https://shellguo.com/osf

DeRF: Decomposed Radiance Fields

Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV、cs.GR

FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework

Lukas Meyer, Andreas Gilson, Ute Schmid, Marc Stamminger

专题命中 NeRF ：point cloud(abstract)；分类 cs.CV；NeRF(comments)

Comments Project Page: https://meyerls.github.io/fruit_nerf/

Mirror-Aware Neural Humans

Daniel Ajisafe, James Tang, Shih-Yang Su, Bastian Wandt, Helge Rhodin

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV；3D vision(comments)

Comments The 11th International Conference on 3D Vision (3DV 2024). Project website: https://danielajisafe.github.io/mirror-aware-neural-humans/

Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis

Jiahe Li, Jiawei Zhang, Xiao Bai, Jun Zhou, Lin Gu

专题命中 NeRF ：NeRF(abstract,comments)；分类 cs.CV

Comments Accepted by ICCV 2023. Project page: https://fictionarry.github.io/ER-NeRF/

BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering

Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao, Anyi Rao, Christian Theobalt, Bo Dai, Dahua Lin

专题命中 NeRF ：NeRF(abstract,comments)；分类 cs.CV

Comments Accepted to ECCV22; Previous version: CityNeRF: Building NeRF at City Scale; Project page can be found in https://city-super.github.io/citynerf

Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos

Sida Peng, Zhen Xu, Junting Dong, Qianqian Wang, Shangzhan Zhang, Qing Shuai, Hujun Bao, Xiaowei Zhou

专题命中 NeRF ：NeRF(abstract,comments)；分类 cs.CV

Comments Project page: https://zju3dv.github.io/animatable_nerf/. arXiv admin note: substantial text overlap with arXiv:2105.02872

PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervision

Konstantinos Tertikas, Despoina Paschalidou, Boxiao Pan, Jeong Joon Park, Mikaela Angelina Uy, Ioannis Emiris, Yannis Avrithis, Leonidas Guibas

专题命中 NeRF ：NeRF(abstract,comments)；分类 cs.CV

Comments To appear in CVPR 2023, Project Page: https://ktertikas.github.io/part_nerf

Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis

Tianhan Xu, Yasuhiro Fujita, Eiichi Matsumoto

专题命中 NeRF ：NeRF(abstract,comments)；分类 cs.CV

Comments CVPR 2022. Project page: https://pfnet-research.github.io/surface-aligned-nerf/

CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis

Peng Zhou, Lingxi Xie, Bingbing Ni, Qi Tian

专题命中 NeRF ：NeRF(abstract,comments)；分类 cs.CV

Comments 3D-aware GANs based on NeRF, https://github.com/PeterouZh/CIPS-3D

StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis

Jiatao Gu, Lingjie Liu, Peng Wang, Christian Theobalt

专题命中 NeRF ：NeRF(abstract,comments)；分类 cs.CV

Comments 24 pages, 19 figures. Project page: http://jiataogu.me/style_nerf/

Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies

Sida Peng, Junting Dong, Qianqian Wang, Shangzhan Zhang, Qing Shuai, Xiaowei Zhou, Hujun Bao

专题命中 NeRF ：NeRF(abstract,comments)；分类 cs.CV

Comments Accepted to ICCV 2021. The first two authors contributed equally to this paper. Project page: https://zju3dv.github.io/animatable_nerf/

Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering

Bangbang Yang, Yinda Zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, Zhaopeng Cui

专题命中 NeRF ：novel view synthesis(abstract)；分类 cs.CV；NeRF(comments)

Comments Accepted to ICCV 2021. Project Page: https://zju3dv.github.io/object_nerf

In-Place Scene Labelling and Understanding with Implicit Scene Representation

Shuaifeng Zhi, Tristan Laidlow, Stefan Leutenegger, Andrew J. Davison

专题命中 NeRF ：NeRF(abstract,comments)；分类 cs.CV

Comments Camera ready version. To be published in Proceedings of IEEE International Conference on Computer Vision (ICCV 2021) as Oral Presentation. Project page with more videos: https://shuaifengzhi.com/Semantic-NeRF/

Portrait Neural Radiance Fields from a Single Image

Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang

专题命中 NeRF ：NeRF(abstract,comments)；分类 cs.CV

Comments Project webpage: https://portrait-nerf.github.io/

BathyFacto: Refraction-Aware Two-Media Neural Radiance Fields for Bathymetry

BathyFacto：考虑折射的双介质神经辐射场用于水深测量

Markus Brezovsky, Anatol Günthner, Frederik Schulte, Lukas Winiwarter, Boris Jutzi, Gottfried Mandlburger

机构 * Department of Geodesy and Geoinformation, TU Wien（维也纳技术大学测绘与地理信息系） ； Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院测绘与遥感研究所） ； Unit of Geometry and Surveying, University of Innsbruck（因斯布鲁克大学几何与测绘单位） 纠错

专题命中 NeRF ：point cloud(abstract)；分类 cs.CV

AI总结 BathyFacto通过整合Nerfacto，利用折射-aware的双介质神经辐射场技术，提高水下点云的测量精度，解决了水面折射带来的系统性深度偏差问题。

Comments 16 pages, 8 figures, 3 tables. Submitted to ISPRS Open Journal of Photogrammetry and Remote Sensing, Special Issue "3D Underwater Mapping from Above and Below"

RehearsalNeRF: Decoupling Intrinsic Neural Fields of Dynamic Illuminations for Scene Editing

RehearsalNeRF：解耦动态光照的内在神经场以实现场景编辑

Changyeon Won, Hyunjun Jung, Jungu Cho, Seonmi Park, Chi-Hoon Lee, Hae-Gon Jeon

机构 * Gwangju Institute of Science and Technology（光州科学技术院） ； Yonsei University（延世大学） ； CJ Corporation（CJ集团） 纠错

专题命中 NeRF ：novel view synthesis(abstract)；分类 cs.CV

AI总结 RehearsalNeRF通过利用稳定光照下的场景数据，解耦动态光照下的场景辐射，提升动态光照下的视图合成和场景编辑性能。

Comments Accepted to the International Journal of Computer Vision (IJCV). Changyeon Won and Hyunjun Jung contributed equally to this work

From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications

从像素到肖像：会说话头像生成技术与应用的全面综述

Shreyank N Gowda, Dheeraj Pandey, Shashank Narayana Gowda

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV

AI总结 综述会说话头像生成技术，将文献分为四类方法并讨论其技术思想等。分析定量指标与感知质量差距，比较公开模型，研究新兴趋势，确定开放挑战，为从业者提供领域地图，突出相关技术和社会问题。

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields

用于增强基于脉冲的神经辐射场的自适应时间步长训练

Ranxi Lin, Canming Yao, Jiayi Li, Weihang Liu, Xin Lou, Pingqiang Zhou

机构 * School of Information Science and Technology, ShanghaiTech University（信息科学与技术学院，上海科技大学） ； Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院） 纠错

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV

AI总结 研究针对基于脉冲的神经辐射场模型固定推理时间步长效率低的问题，提出PATA框架，通过参数化目标推理时间步长并两阶段训练，结合多种策略降低推理成本并保持渲染质量。

Real-time 3D Visualization of Radiance Fields on Light Field Displays

光场显示上辐射场的实时3D可视化

Jonghyun Kim, Cheng Sun, Michael Stengel, Matthew Chan, Andrew Russell, Jaehyun Jung, Wil Braithwaite, David Luebke, Shalini De Mello

专题命中 NeRF ：Gaussian Splatting(abstract)；分类 cs.GR

AI总结 针对光场显示需要多视角高分辨率渲染而辐射场计算密集的问题，提出统一高效框架，通过共享中间扫描平面单次合成密集光场视图，实现实时渲染（200+ FPS），速度提升22倍。

Comments 19 pages, 14 figures. J. Kim, C. Sun, and M. Stengel contributed equally

Safe Navigation using Neural Radiance Fields via Reachable Sets

通过可达集实现神经辐射场的安全导航

Omanshu Thapliyal, Malarvizhi Sankaranarayanasamy, Ravigopal Vennelakanti

机构 * Researcher, Hitachi America Ltd.（Hitachi America Ltd.研究人员） ； Sr. Researcher, Hitachi America Ltd.（Hitachi America Ltd.高级研究人员） 纠错

专题命中 NeRF ：NeRF(abstract_cn)；分类 cs.RO

AI总结 本文提出利用可达集和神经辐射场进行安全导航，通过约束最优控制解决路径规划问题，验证了在复杂环境中安全避障的能力。

Comments 5 pages, 8 figures, 2026 4th International Conference on Mechatronics, Control and Robotics (ICMCR)

Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark

暗光环境下的辐射场重建：事件相机作为视觉的眼睛

Jingqian Wu, Peiqi Duan, Zongqiang Wang, Changwei Wang, Boxin Shi, Edmund Y. Lam

机构 * Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电机电子工程系） ； State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室及视觉技术国家工程研究中心） ； Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所） ； Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology（齐鲁工业大学（山东省科学院）山东省计算中心（国家超级计算济南中心）计算力网络与信息安全教育部重点实验室） 纠错

专题命中 NeRF ：Gaussian Splatting(abstract)；分类 cs.CV

AI总结 本文提出Dark-EvGS框架，利用事件相机和3D高斯点绘制技术，在暗光环境中实现高质量辐射场重建，通过三重监督和颜色匹配模块提升合成图像质量。

AccidentSim: Generating Vehicle Collision Videos with Physically Realistic Collision Trajectories from Real-World Accident Reports

AccidentSim: 从真实世界事故报告生成具有物理真实碰撞轨迹的车辆碰撞视频

Xiangwen Zhang, Qian Zhang, Longfei Han, Qiang Qu, Xiaoming Chen, Weidong Cai

机构 * School of Computer and Artificial Intelligence, Beijing Technology and Business University（北京工商大学计算机与人工智能学院） ； The University of Sydney（悉尼大学） 纠错

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV

AI总结 AccidentSim通过提取事故报告中的物理信息生成真实碰撞视频，结合物理模拟和NeRF技术提升视觉与物理真实性。

Factorized Multi-Resolution HashGrid for Efficient Neural Radiance Fields: Execution on Edge-Devices

因子化多分辨率哈希网格：用于高效神经辐射场的执行边缘设备

Kim Jun-Seong, Mingyu Kim, GeonU Kim, Tae-Hyun Oh, Jin-Hwa Kim

机构 * POSTECH（浦项科技大学） ； KAIST（韩国科学技术院） ； NAVER AI Lab（NAVER AI实验室） 纠错

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV

AI总结 本文提出Fact-Hash，一种用于边缘设备神经辐射场训练的参数编码方法，通过融合张量分解与哈希编码技术，提升内存效率和渲染速度，实现高质量3D表示。

Comments Accepted for publication in IEEE Robotics and Automation Letters (RA-L)

Journal ref IEEE Robotics and Automation Letters (RA-L), 2024

Simple-RF: Regularizing Sparse Input Radiance Fields with Simpler Solutions

Simple-RF: 通过更简单的解决方案正则化稀疏输入辐射场

Nagabhushan Somraj, Sai Harsha Mupparaju, Adithyan Karanayil, Rajiv Soundararajan

机构 * Indian Institute of Science（印度科学研究所） 纠错

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV

AI总结 本文提出Simple-RF方法，通过设计增强模型和正则化框架，改进稀疏输入下辐射场的深度估计，从而在稀疏视图条件下实现更优的视图合成性能。

Comments The source code for our model can be found on our project page: https://nagabhushansn95.github.io/publications/2024/Simple-RF.html. Extension of arXiv:2309.03955

Node-RF: Learning Generalized Continuous Space-Time Scene Dynamics with Neural ODE-based NeRFs

Node-RF：基于神经ODE的NeRF学习通用连续时空场景动态

Hiran Sarkar, Liming Kuang, Yordanka Velikova, Benjamin Busam

机构 * Technical University of Munich（慕尼黑技术大学） ； Munich Center for Machine Learning（慕尼黑机器学习中心） 纠错

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV

AI总结 Node-RF通过结合神经ODE与动态NeRF，实现连续时间空间动态表示，能超越观测轨迹进行泛化。利用ODE求解器学习隐式场景状态，通过微分计算传播特征嵌入，实现长距离外推。

Comments Accepted to CVPR 2026. 13 pages, 9 figures

Weight Conditioning for Smooth Optimization of Neural Networks

权重条件化以实现神经网络的平滑优化

Hemanth Saratchandran, Thomas X. Wang, Simon Lucey

机构 * Australian Institute for Machine Learning, University of Adelaide（澳大利亚机器学习研究所，阿德莱德大学） ； Sorbonne Université, CNRS, ISIR（索邦大学，法国国家科学研究中心，ISIR） 纠错

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV

AI总结 本文提出一种新的权重规范化技术，通过缩小权重矩阵的奇异值范围，提升矩阵条件数，从而改善梯度下降收敛性能，实验验证其在多种神经网络架构中的有效性。

Comments ECCV 2024

Towards 3D Scene Understanding of Gas Plumes in LWIR Hyperspectral Images Using Neural Radiance Fields

利用神经辐射场实现L波段超光谱图像中气体云三维场景理解

Scout Jarman, Zigfried Hampel-Arias, Adra Carr, Kevin R. Moon

机构 * Utah State University, Department of Mathematics & Statistics（犹他州立大学数学与统计学系） ； Los Alamos National Laboratory, Intelligence and Space Research Division（洛斯阿拉莫斯国家实验室情报与空间研究部） 纠错

专题命中 NeRF ：NeRF(abstract)；分类 cs.CV

AI总结 本文提出一种基于神经辐射场的三维场景重建方法，利用较少训练图像实现气体云检测，提升L波段超光谱图像中气体云的分析能力。

Comments This manuscript was submitted to SPIE JARS and is under review. Code and Data can be found at https://github.com/lanl/HSI-Nerfstudio and https://zenodo.org/records/18626884 respectively. Video 1 and Video 2 can be found at https://github.com/lanl/HSI-Nerfstudio/blob/main/renders/paper/grid_Falsecolor.mp4 and https://github.com/lanl/HSI-Nerfstudio/blob/main/renders/paper/grid_ACE.mp4 respectively