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
NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections
Dor Verbin, Pratul P. Srinivasan, Peter Hedman, Ben Mildenhall, Benjamin Attal, Richard Szeliski, Jonathan T. Barron
Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling
Liwen Wu, Sai Bi, Zexiang Xu, Fujun Luan, Kai Zhang, Iliyan Georgiev, Kalyan Sunkavalli, Ravi Ramamoorthi
Camera Relocalization in Shadow-free Neural Radiance Fields
Shiyao Xu, Caiyun Liu, Yuantao Chen, Zhenxin Zhu, Zike Yan, Yongliang Shi, Hao Zhao, Guyue Zhou
JointRF: End-to-End Joint Optimization for Dynamic Neural Radiance Field Representation and Compression
Zihan Zheng, Houqiang Zhong, Qiang Hu, Xiaoyun Zhang, Li Song, Ya Zhang, Yanfeng Wang
Tactile-Augmented Radiance Fields
Yiming Dou, Fengyu Yang, Yi Liu, Antonio Loquercio, Andrew Owens
Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications
Markus Hillemann, Robert Langendörfer, Max Heiken, Max Mehltretter, Andreas Schenk, Martin Weinmann, Stefan Hinz, Christian Heipke, Markus Ulrich