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
HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
Bipasha Sen, Gaurav Singh, Aditya Agarwal, Rohith Agaram, K Madhava Krishna, Srinath Sridhar
GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields
Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Matthias Nießner
RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models
Xingchen Zhou, Ying He, F. Richard Yu, Jianqiang Li, You Li
LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs
Zezhou Cheng, Carlos Esteves, Varun Jampani, Abhishek Kar, Subhransu Maji, Ameesh Makadia
Enhance-NeRF: Multiple Performance Evaluation for Neural Radiance Fields
Qianqiu Tan, Tao Liu, Yinling Xie, Shuwan Yu, Baohua Zhang
Variable Radiance Field for Real-Life Category-Specifc Reconstruction from Single Image
Kun Wang, Zhiqiang Yan, Zhenyu Zhang, Xiang Li, Jun Li, Jian Yang
DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation
Jiuhn Song, Seonghoon Park, Honggyu An, Seokju Cho, Min-Seop Kwak, Sungjin Cho, Seungryong Kim
Template-free Articulated Neural Point Clouds for Reposable View Synthesis
Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer
HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance
Junzhe Zhu, Peiye Zhuang, Sanmi Koyejo
Compact Real-time Radiance Fields with Neural Codebook
Lingzhi Li, Zhongshu Wang, Zhen Shen, Li Shen, Ping Tan
Towards a Robust Framework for NeRF Evaluation
Adrian Azzarelli, Nantheera Anantrasirichai, David R Bull
Volume Feature Rendering for Fast Neural Radiance Field Reconstruction
Kang Han, Wei Xiang, Lu Yu