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
RigNeRF: Fully Controllable Neural 3D Portraits
ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, Zhixin Shu
SNeS: Learning Probably Symmetric Neural Surfaces from Incomplete Data
Eldar Insafutdinov, Dylan Campbell, João F. Henriques, Andrea Vedaldi
AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields
Takuhiro Kaneko
D$^2$NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video
Tianhao Wu, Fangcheng Zhong, Andrea Tagliasacchi, Forrester Cole, Cengiz Oztireli
DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes
Jia-Wei Liu, Yan-Pei Cao, Weijia Mao, Wenqiao Zhang, David Junhao Zhang, Jussi Keppo, Ying Shan, Xiaohu Qie, Mike Zheng Shou
Novel View Synthesis for High-fidelity Headshot Scenes
Satoshi Tsutsui, Weijia Mao, Sijing Lin, Yunyi Zhu, Murong Ma, Mike Zheng Shou
Decomposing NeRF for Editing via Feature Field Distillation
Sosuke Kobayashi, Eiichi Matsumoto, Vincent Sitzmann
Fast Dynamic Radiance Fields with Time-Aware Neural Voxels
Jiemin Fang, Taoran Yi, Xinggang Wang, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Matthias Nießner, Qi Tian
Neural Volumetric Object Selection
Zhongzheng Ren, Aseem Agarwala, Bryan Russell, Alexander G. Schwing, Oliver Wang
Compressible-composable NeRF via Rank-residual Decomposition
Jiaxiang Tang, Xiaokang Chen, Jingbo Wang, Gang Zeng