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
DT-NeRF: Decomposed Triplane-Hash Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis
Yaoyu Su, Shaohui Wang, Haoqian Wang
CoRF : Colorizing Radiance Fields using Knowledge Distillation
Ankit Dhiman, R Srinath, Srinjay Sarkar, Lokesh R Boregowda, R Venkatesh Babu
Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement
Sheng Ye, Yubin Hu, Matthieu Lin, Yu-Hui Wen, Wang Zhao, Yong-Jin Liu, Wenping Wang
SimpleNeRF: Regularizing Sparse Input Neural Radiance Fields with Simpler Solutions
Nagabhushan Somraj, Adithyan Karanayil, Rajiv Soundararajan
BluNF: Blueprint Neural Field
Robin Courant, Xi Wang, Marc Christie, Vicky Kalogeiton
SimNP: Learning Self-Similarity Priors Between Neural Points
Christopher Wewer, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen
Text2Control3D: Controllable 3D Avatar Generation in Neural Radiance Fields using Geometry-Guided Text-to-Image Diffusion Model
Sungwon Hwang, Junha Hyung, Jaegul Choo
Efficient Ray Sampling for Radiance Fields Reconstruction
Shilei Sun, Ming Liu, Zhongyi Fan, Yuxue Liu, Chengwei Lv, Liquan Dong, Lingqin Kong
Pose-Free Neural Radiance Fields via Implicit Pose Regularization
Jiahui Zhang, Fangneng Zhan, Yingchen Yu, Kunhao Liu, Rongliang Wu, Xiaoqin Zhang, Ling Shao, Shijian Lu