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
PointNeRF++: A multi-scale, point-based Neural Radiance Field
Weiwei Sun, Eduard Trulls, Yang-Che Tseng, Sneha Sambandam, Gopal Sharma, Andrea Tagliasacchi, Kwang Moo Yi
Instant Uncertainty Calibration of NeRFs Using a Meta-Calibrator
Niki Amini-Naieni, Tomas Jakab, Andrea Vedaldi, Ronald Clark
Mesh-Guided Neural Implicit Field Editing
Can Wang, Mingming He, Menglei Chai, Dongdong Chen, Jing Liao
Re-Nerfing: Improving Novel View Synthesis through Novel View Synthesis
Felix Tristram, Stefano Gasperini, Nassir Navab, Federico Tombari
ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy
Yufei Shi, Beijia Lu, Jia-Wei Liu, Ming Li, Mike Zheng Shou
PyNeRF: Pyramidal Neural Radiance Fields
Haithem Turki, Michael Zollhöfer, Christian Richardt, Deva Ramanan
SparseGS: Real-Time 360{\deg} Sparse View Synthesis using Gaussian Splatting
Haolin Xiong, Sairisheek Muttukuru, Rishi Upadhyay, Pradyumna Chari, Achuta Kadambi
Redefining Recon: Bridging Gaps with UAVs, 360 degree Cameras, and Neural Radiance Fields
Hartmut Surmann, Niklas Digakis, Jan-Nicklas Kremer, Julien Meine, Max Schulte, Niklas Voigt
Anisotropic Neural Representation Learning for High-Quality Neural Rendering
Y. Wang, J. Xu, Y. Zeng, Y. Gong
FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Information
Wen Jiang, Boshu Lei, Kostas Daniilidis
NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields
Xiaoliang Liu, Furao Shen, Feng Han, Jian Zhao, Changhai Nie
Rethinking Directional Integration in Neural Radiance Fields
Congyue Deng, Jiawei Yang, Leonidas Guibas, Yue Wang
UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving
Kai Cheng, Xiaoxiao Long, Wei Yin, Jin Wang, Zhiqiang Wu, Yuexin Ma, Kaixuan Wang, Xiaozhi Chen, Xuejin Chen
REF$^2$-NeRF: Reflection and Refraction aware Neural Radiance Field
Wooseok Kim, Taiki Fukiage, Takeshi Oishi
SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation
Jesus Zarzar, Bernard Ghanem
DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes
Zhuopeng Li, Chenming Wu, Liangjun Zhang, Jianke Zhu