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
Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids
Junchen Liu, Wenbo Hu, Zhuo Yang, Jianteng Chen, Guoliang Wang, Xiaoxue Chen, Yantong Cai, Huan-ang Gao, Hao Zhao
WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights
Youngdong Jang, Dong In Lee, MinHyuk Jang, Jong Wook Kim, Feng Yang, Sangpil Kim
LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes
Shanlin Sun, Bingbing Zhuang, Ziyu Jiang, Buyu Liu, Xiaohui Xie, Manmohan Chandraker
Depth Priors in Removal Neural Radiance Fields
Zhihao Guo, Peng Wang
NeRF-Guided Unsupervised Learning of RGB-D Registration
Zhinan Yu, Zheng Qin, Yijie Tang, Yongjun Wang, Renjiao Yi, Chenyang Zhu, Kai Xu
Embedded Representation Learning Network for Animating Styled Video Portrait
Tianyong Wang, Xiangyu Liang, Wangguandong Zheng, Dan Niu, Haifeng Xia, Siyu Xia
Simple-RF: Regularizing Sparse Input Radiance Fields with Simpler Solutions
Nagabhushan Somraj, Sai Harsha Mupparaju, Adithyan Karanayil, Rajiv Soundararajan
CT-NeRF: Incremental Optimizing Neural Radiance Field and Poses with Complex Trajectory
Yunlong Ran, Yanxu Li, Qi Ye, Yuchi Huo, Zechun Bai, Jiahao Sun, Jiming Chen
Neural Radiance Field in Autonomous Driving: A Survey
Lei He, Leheng Li, Wenchao Sun, Zeyu Han, Yichen Liu, Sifa Zheng, Jianqiang Wang, Keqiang Li