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
Feasibility of Neural Radiance Fields for Crime Scene Video Reconstruction
Shariq Nadeem Malik, Min Hao Chee, Dayan Mario Anthony Perera, Chern Hong Lim
MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos
Yushuo Chen, Zerong Zheng, Zhe Li, Chao Xu, Yebin Liu
Explicit-NeRF-QA: A Quality Assessment Database for Explicit NeRF Model Compression
Yuke Xing, Qi Yang, Kaifa Yang, Yilin Xu, Zhu Li
Survey on Fundamental Deep Learning 3D Reconstruction Techniques
Yonge Bai, LikHang Wong, TszYin Twan
Enhancing Neural Radiance Fields with Depth and Normal Completion Priors from Sparse Views
Jiawei Guo, HungChyun Chou, Ning Ding
GeoNLF: Geometry guided Pose-Free Neural LiDAR Fields
Weiyi Xue, Zehan Zheng, Fan Lu, Haiyun Wei, Guang Chen, Changjun Jiang
Dynamic Neural Radiance Field From Defocused Monocular Video
Xianrui Luo, Huiqiang Sun, Juewen Peng, Zhiguo Cao
fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence
Francis Williams, Jiahui Huang, Jonathan Swartz, Gergely Klár, Vijay Thakkar, Matthew Cong, Xuanchi Ren, Ruilong Li, Clement Fuji-Tsang, Sanja Fidler, Eftychios Sifakis, Ken Museth
RoDyn-SLAM: Robust Dynamic Dense RGB-D SLAM with Neural Radiance Fields
Haochen Jiang, Yueming Xu, Kejie Li, Jianfeng Feng, Li Zhang