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
SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields
Quentin Herau, Nathan Piasco, Moussab Bennehar, Luis Roldão, Dzmitry Tsishkou, Cyrille Migniot, Pascal Vasseur, Cédric Demonceaux
Animatable 3D Gaussian: Fast and High-Quality Reconstruction of Multiple Human Avatars
Yang Liu, Xiang Huang, Minghan Qin, Qinwei Lin, Haoqian Wang
CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering
Haidong Zhu, Tianyu Ding, Tianyi Chen, Ilya Zharkov, Ram Nevatia, Luming Liang
Compact 3D Gaussian Representation for Radiance Field
Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, Eunbyung Park
Retargeting Visual Data with Deformation Fields
Tim Elsner, Julia Berger, Tong Wu, Victor Czech, Lin Gao, Leif Kobbelt
3D Face Style Transfer with a Hybrid Solution of NeRF and Mesh Rasterization
Jianwei Feng, Prateek Singhal