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
CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout
Haotian Bai, Yuanhuiyi Lyu, Lutao Jiang, Sijia Li, Haonan Lu, Xiaodong Lin, Lin Wang
HandNeRF: Neural Radiance Fields for Animatable Interacting Hands
Zhiyang Guo, Wengang Zhou, Min Wang, Li Li, Houqiang Li
ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field
Zhe Jun Tang, Tat-Jen Cham, Haiyu Zhao
TEGLO: High Fidelity Canonical Texture Mapping from Single-View Images
Vishal Vinod, Tanmay Shah, Dmitry Lagun
SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates
Mikaela Angelina Uy, Ricardo Martin-Brualla, Leonidas Guibas, Ke Li
Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes
Dana Cohen-Bar, Elad Richardson, Gal Metzer, Raja Giryes, Daniel Cohen-Or
Transforming Radiance Field with Lipschitz Network for Photorealistic 3D Scene Stylization
Zicheng Zhang, Yinglu Liu, Congying Han, Yingwei Pan, Tiande Guo, Ting Yao
Pre-NeRF 360: Enriching Unbounded Appearances for Neural Radiance Fields
Ahmad AlMughrabi, Umair Haroon, Ricardo Marques, Petia Radeva
Few-shot Neural Radiance Fields Under Unconstrained Illumination
SeokYeong Lee, JunYong Choi, Seungryong Kim, Ig-Jae Kim, Junghyun Cho
Interactive Geometry Editing of Neural Radiance Fields
Shaoxu Li, Ye Pan
$\alpha$Surf: Implicit Surface Reconstruction for Semi-Transparent and Thin Objects with Decoupled Geometry and Opacity
Tianhao Wu, Hanxue Liang, Fangcheng Zhong, Gernot Riegler, Shimon Vainer, Cengiz Oztireli
Single-view Neural Radiance Fields with Depth Teacher
Yurui Chen, Chun Gu, Feihu Zhang, Li Zhang
NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes
Marie-Julie Rakotosaona, Fabian Manhardt, Diego Martin Arroyo, Michael Niemeyer, Abhijit Kundu, Federico Tombari
NeRFtrinsic Four: An End-To-End Trainable NeRF Jointly Optimizing Diverse Intrinsic and Extrinsic Camera Parameters
Hannah Schieber, Fabian Deuser, Bernhard Egger, Norbert Oswald, Daniel Roth