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
GraphAvatar: Compact Head Avatars with GNN-Generated 3D Gaussians
Xiaobao Wei, Peng Chen, Ming Lu, Hui Chen, Feng Tian
RelationField: Relate Anything in Radiance Fields
Sebastian Koch, Johanna Wald, Mirco Colosi, Narunas Vaskevicius, Pedro Hermosilla, Federico Tombari, Timo Ropinski
DreaMark: Rooting Watermark in Score Distillation Sampling Generated Neural Radiance Fields
Xingyu Zhu, Xiapu Luo, Xuetao Wei
AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models
Tommy Nguyen, Mehmet Ergezer, Christian Green