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
Entity-NeRF: Detecting and Removing Moving Entities in Urban Scenes
Takashi Otonari, Satoshi Ikehata, Kiyoharu Aizawa
Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap
Carl Lindström, Georg Hess, Adam Lilja, Maryam Fatemi, Lars Hammarstrand, Christoffer Petersson, Lennart Svensson
Semantic Is Enough: Only Semantic Information For NeRF Reconstruction
Ruibo Wang, Song Zhang, Ping Huang, Donghai Zhang, Wei Yan
Exploring Accurate 3D Phenotyping in Greenhouse through Neural Radiance Fields
Junhong Zhao, Wei Ying, Yaoqiang Pan, Zhenfeng Yi, Chao Chen, Kewei Hu, Hanwen Kang
DriveEnv-NeRF: Exploration of A NeRF-Based Autonomous Driving Environment for Real-World Performance Validation
Mu-Yi Shen, Chia-Chi Hsu, Hao-Yu Hou, Yu-Chen Huang, Wei-Fang Sun, Chia-Che Chang, Yu-Lun Liu, Chun-Yi Lee
Gaussian in the Wild: 3D Gaussian Splatting for Unconstrained Image Collections
Dongbin Zhang, Chuming Wang, Weitao Wang, Peihao Li, Minghan Qin, Haoqian Wang
CombiNeRF: A Combination of Regularization Techniques for Few-Shot Neural Radiance Field View Synthesis
Matteo Bonotto, Luigi Sarrocco, Daniele Evangelista, Marco Imperoli, Alberto Pretto
InfNeRF: Towards Infinite Scale NeRF Rendering with O(log n) Space Complexity
Jiabin Liang, Lanqing Zhang, Zhuoran Zhao, Xiangyu Xu
Leveraging Thermal Modality to Enhance Reconstruction in Low-Light Conditions
Jiacong Xu, Mingqian Liao, K Ram Prabhakar, Vishal M. Patel
Depth-guided NeRF Training via Earth Mover's Distance
Anita Rau, Josiah Aklilu, F. Christopher Holsinger, Serena Yeung-Levy
DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images
Zaid Tasneem, Akshat Dave, Abhishek Singh, Kushagra Tiwary, Praneeth Vepakomma, Ashok Veeraraghavan, Ramesh Raskar
IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single image and a NeRF model
Matteo Bortolon, Theodore Tsesmelis, Stuart James, Fabio Poiesi, Alessio Del Bue
ThermoNeRF: Multimodal Neural Radiance Fields for Thermal Novel View Synthesis
Mariam Hassan, Florent Forest, Olga Fink, Malcolm Mielle
GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors
LI Yang, WU Ruizheng, LI Jiyong, CHEN Ying-cong
Exploring Multi-modal Neural Scene Representations With Applications on Thermal Imaging
Mert Özer, Maximilian Weiherer, Martin Hundhausen, Bernhard Egger
BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting
Lingzhe Zhao, Peng Wang, Peidong Liu
Aerial Lifting: Neural Urban Semantic and Building Instance Lifting from Aerial Imagery
Yuqi Zhang, Guanying Chen, Jiaxing Chen, Shuguang Cui