Neural Radiance Field
Neural Radiance Fields (NeRFs) are a powerful technique for creating realistic 3D scene representations from 2D images, aiming to reconstruct both geometry and appearance. Current research focuses on improving efficiency and robustness, exploring variations like Gaussian splatting for faster rendering and adapting NeRFs for diverse data modalities (LiDAR, infrared, ultrasound) and challenging conditions (low light, sparse views). This technology has significant implications for various fields, including autonomous driving, robotics, medical imaging, and virtual/augmented reality, by enabling high-fidelity 3D scene modeling and novel view synthesis from limited input data.
Papers
WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields
Muyu Xu, Fangneng Zhan, Jiahui Zhang, Yingchen Yu, Xiaoqin Zhang, Christian Theobalt, Ling Shao, Shijian Lu
A General Implicit Framework for Fast NeRF Composition and Rendering
Xinyu Gao, Ziyi Yang, Yunlu Zhao, Yuxiang Sun, Xiaogang Jin, Changqing Zou
Seal-3D: Interactive Pixel-Level Editing for Neural Radiance Fields
Xiangyu Wang, Jingsen Zhu, Qi Ye, Yuchi Huo, Yunlong Ran, Zhihua Zhong, Jiming Chen
MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving
Zirui Wu, Tianyu Liu, Liyi Luo, Zhide Zhong, Jianteng Chen, Hongmin Xiao, Chao Hou, Haozhe Lou, Yuantao Chen, Runyi Yang, Yuxin Huang, Xiaoyu Ye, Zike Yan, Yongliang Shi, Yiyi Liao, Hao Zhao
Improved Neural Radiance Fields Using Pseudo-depth and Fusion
Jingliang Li, Qiang Zhou, Chaohui Yu, Zhengda Lu, Jun Xiao, Zhibin Wang, Fan Wang
MapNeRF: Incorporating Map Priors into Neural Radiance Fields for Driving View Simulation
Chenming Wu, Jiadai Sun, Zhelun Shen, Liangjun Zhang
Dyn-E: Local Appearance Editing of Dynamic Neural Radiance Fields
Shangzhan Zhang, Sida Peng, Yinji ShenTu, Qing Shuai, Tianrun Chen, Kaicheng Yu, Hujun Bao, Xiaowei Zhou
CarPatch: A Synthetic Benchmark for Radiance Field Evaluation on Vehicle Components
Davide Di Nucci, Alessandro Simoni, Matteo Tomei, Luca Ciuffreda, Roberto Vezzani, Rita Cucchiara
CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields
Ziyuan Luo, Qing Guo, Ka Chun Cheung, Simon See, Renjie Wan
FaceCLIPNeRF: Text-driven 3D Face Manipulation using Deformable Neural Radiance Fields
Sungwon Hwang, Junha Hyung, Daejin Kim, Min-Jung Kim, Jaegul Choo
Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fields
Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, Yuewen Ma