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
Gaussian Splashing: Direct Volumetric Rendering Underwater
Nir Mualem, Roy Amoyal, Oren Freifeld, Derya Akkaynak
LokiTalk: Learning Fine-Grained and Generalizable Correspondences to Enhance NeRF-based Talking Head Synthesis
Tianqi Li, Ruobing Zheng, Bonan Li, Zicheng Zhang, Meng Wang, Jingdong Chen, Ming Yang
Towards More Accurate Fake Detection on Images Generated from Advanced Generative and Neural Rendering Models
Chengdong Dong, Vijayakumar Bhagavatula, Zhenyu Zhou, Ajay Kumar
MBA-SLAM: Motion Blur Aware Dense Visual SLAM with Radiance Fields Representation
Peng Wang, Lingzhe Zhao, Yin Zhang, Shiyu Zhao, Peidong Liu
CAD-NeRF: Learning NeRFs from Uncalibrated Few-view Images by CAD Model Retrieval
Xin Wen, Xuening Zhu, Renjiao Yi, Zhifeng Wang, Chenyang Zhu, Kai Xu
Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery
Liv Kåreborn, Erica Ingerstad, Amanda Berg, Justus Karlsson, Leif Haglund
NeRF-Aug: Data Augmentation for Robotics with Neural Radiance Fields
Eric Zhu, Mara Levy, Matthew Gwilliam, Abhinav Shrivastava
GVKF: Gaussian Voxel Kernel Functions for Highly Efficient Surface Reconstruction in Open Scenes
Gaochao Song, Chong Cheng, Hao Wang
A Probabilistic Formulation of LiDAR Mapping with Neural Radiance Fields
Matthew McDermott, Jason Rife